{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "github主页: https://github.com/ZhengRyan/autobmt\n",
    "实验数据: 链接: https://pan.baidu.com/s/1BRIHH9Wcwy2EZaO5xSgH9w?pwd=tdq5 提取码: tdq5"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "安装与升级：\n",
    "1. pip安装：!pip install autobmt \n",
    "2. conda安装：!conda install autobmt –channel conda-forge \n",
    "3. pip升级：!pip install -U autobmt\n",
    "4. conda升级：!conda install -U autobmt –channel conda-forge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import autobmt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.1.4'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autobmt.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 读数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (108940, 168)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APP_ID_C</th>\n",
       "      <th>target</th>\n",
       "      <th>var_d1</th>\n",
       "      <th>var_d2</th>\n",
       "      <th>var_d3</th>\n",
       "      <th>var_d4</th>\n",
       "      <th>var_d5</th>\n",
       "      <th>var_d6</th>\n",
       "      <th>var_d7</th>\n",
       "      <th>var_d8</th>\n",
       "      <th>var_d9</th>\n",
       "      <th>var_d11</th>\n",
       "      <th>var_b1</th>\n",
       "      <th>var_b2</th>\n",
       "      <th>var_b3</th>\n",
       "      <th>var_b4</th>\n",
       "      <th>var_b5</th>\n",
       "      <th>var_b6</th>\n",
       "      <th>var_b7</th>\n",
       "      <th>var_b8</th>\n",
       "      <th>var_b9</th>\n",
       "      <th>var_b10</th>\n",
       "      <th>var_b11</th>\n",
       "      <th>var_b12</th>\n",
       "      <th>var_b13</th>\n",
       "      <th>var_b14</th>\n",
       "      <th>var_b15</th>\n",
       "      <th>var_b16</th>\n",
       "      <th>var_b17</th>\n",
       "      <th>var_b18</th>\n",
       "      <th>var_b19</th>\n",
       "      <th>var_b20</th>\n",
       "      <th>var_b21</th>\n",
       "      <th>var_b22</th>\n",
       "      <th>var_b23</th>\n",
       "      <th>var_b24</th>\n",
       "      <th>var_b25</th>\n",
       "      <th>var_b26</th>\n",
       "      <th>var_b27</th>\n",
       "      <th>var_b28</th>\n",
       "      <th>var_l_1</th>\n",
       "      <th>var_l_2</th>\n",
       "      <th>var_l_3</th>\n",
       "      <th>var_l_4</th>\n",
       "      <th>var_l_5</th>\n",
       "      <th>var_l_6</th>\n",
       "      <th>var_l_7</th>\n",
       "      <th>var_l_8</th>\n",
       "      <th>var_l_9</th>\n",
       "      <th>var_l_10</th>\n",
       "      <th>var_l_11</th>\n",
       "      <th>var_l_12</th>\n",
       "      <th>var_l_13</th>\n",
       "      <th>var_l_14</th>\n",
       "      <th>var_l_15</th>\n",
       "      <th>var_l_16</th>\n",
       "      <th>var_l_17</th>\n",
       "      <th>var_l_18</th>\n",
       "      <th>var_l_19</th>\n",
       "      <th>var_l_20</th>\n",
       "      <th>var_l_21</th>\n",
       "      <th>var_l_22</th>\n",
       "      <th>var_l_23</th>\n",
       "      <th>var_l_24</th>\n",
       "      <th>var_l_25</th>\n",
       "      <th>var_l_26</th>\n",
       "      <th>var_l_27</th>\n",
       "      <th>var_l_28</th>\n",
       "      <th>var_l_29</th>\n",
       "      <th>var_l_30</th>\n",
       "      <th>var_l_31</th>\n",
       "      <th>var_l_32</th>\n",
       "      <th>var_l_33</th>\n",
       "      <th>var_l_34</th>\n",
       "      <th>var_l_35</th>\n",
       "      <th>var_l_36</th>\n",
       "      <th>var_l_37</th>\n",
       "      <th>var_l_38</th>\n",
       "      <th>var_l_39</th>\n",
       "      <th>var_l_40</th>\n",
       "      <th>var_l_41</th>\n",
       "      <th>var_l_42</th>\n",
       "      <th>var_l_43</th>\n",
       "      <th>var_l_44</th>\n",
       "      <th>var_l_45</th>\n",
       "      <th>var_l_46</th>\n",
       "      <th>var_l_47</th>\n",
       "      <th>var_l_48</th>\n",
       "      <th>var_l_49</th>\n",
       "      <th>var_l_50</th>\n",
       "      <th>var_l_51</th>\n",
       "      <th>var_l_52</th>\n",
       "      <th>var_l_53</th>\n",
       "      <th>var_l_54</th>\n",
       "      <th>var_l_55</th>\n",
       "      <th>var_l_56</th>\n",
       "      <th>var_l_57</th>\n",
       "      <th>var_l_58</th>\n",
       "      <th>var_l_59</th>\n",
       "      <th>var_l_60</th>\n",
       "      <th>var_l_61</th>\n",
       "      <th>var_l_62</th>\n",
       "      <th>var_l_63</th>\n",
       "      <th>var_l_64</th>\n",
       "      <th>var_l_65</th>\n",
       "      <th>var_l_66</th>\n",
       "      <th>var_l_67</th>\n",
       "      <th>var_l_68</th>\n",
       "      <th>var_l_69</th>\n",
       "      <th>var_l_70</th>\n",
       "      <th>var_l_71</th>\n",
       "      <th>var_l_72</th>\n",
       "      <th>var_l_73</th>\n",
       "      <th>var_l_74</th>\n",
       "      <th>var_l_75</th>\n",
       "      <th>var_l_76</th>\n",
       "      <th>var_l_77</th>\n",
       "      <th>var_l_78</th>\n",
       "      <th>var_l_79</th>\n",
       "      <th>var_l_80</th>\n",
       "      <th>var_l_81</th>\n",
       "      <th>var_l_82</th>\n",
       "      <th>var_l_83</th>\n",
       "      <th>var_l_84</th>\n",
       "      <th>var_l_85</th>\n",
       "      <th>var_l_86</th>\n",
       "      <th>var_l_87</th>\n",
       "      <th>var_l_88</th>\n",
       "      <th>var_l_89</th>\n",
       "      <th>var_l_90</th>\n",
       "      <th>var_l_91</th>\n",
       "      <th>var_l_92</th>\n",
       "      <th>var_l_93</th>\n",
       "      <th>var_l_94</th>\n",
       "      <th>var_l_95</th>\n",
       "      <th>var_l_96</th>\n",
       "      <th>var_l_97</th>\n",
       "      <th>var_l_98</th>\n",
       "      <th>var_l_99</th>\n",
       "      <th>var_l_100</th>\n",
       "      <th>var_l_101</th>\n",
       "      <th>var_l_102</th>\n",
       "      <th>var_l_103</th>\n",
       "      <th>var_l_104</th>\n",
       "      <th>var_l_105</th>\n",
       "      <th>var_l_106</th>\n",
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       "      <th>var_l_108</th>\n",
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       "      <th>var_l_110</th>\n",
       "      <th>var_l_111</th>\n",
       "      <th>var_l_112</th>\n",
       "      <th>var_l_113</th>\n",
       "      <th>var_l_114</th>\n",
       "      <th>var_l_115</th>\n",
       "      <th>var_l_116</th>\n",
       "      <th>var_l_117</th>\n",
       "      <th>var_l_118</th>\n",
       "      <th>var_l_119</th>\n",
       "      <th>var_l_120</th>\n",
       "      <th>var_l_121</th>\n",
       "      <th>var_l_122</th>\n",
       "      <th>var_l_123</th>\n",
       "      <th>var_l_124</th>\n",
       "      <th>var_l_125</th>\n",
       "      <th>var_l_126</th>\n",
       "      <th>type</th>\n",
       "      <th>apply_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>app_1</td>\n",
       "      <td>0</td>\n",
       "      <td>Hit-6+ Vintage</td>\n",
       "      <td>816.0</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>Post-Graduate</td>\n",
       "      <td>M</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>SELF-EMPLOYED</td>\n",
       "      <td>Y</td>\n",
       "      <td>-11111</td>\n",
       "      <td>N</td>\n",
       "      <td>-9999</td>\n",
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       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>app_2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>841.0</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>Post-Graduate</td>\n",
       "      <td>F</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>N</td>\n",
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       "      <td>12</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>app_3</td>\n",
       "      <td>0</td>\n",
       "      <td>Hit-6+ Vintage</td>\n",
       "      <td>791.0</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>Post-Graduate</td>\n",
       "      <td>M</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>PROPRIETOR</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-8888</td>\n",
       "      <td>0.824694</td>\n",
       "      <td>8</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>0.824694</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019300</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004975</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000775</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003663</td>\n",
       "      <td>0.001085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001682</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001256</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.318331</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013013</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.002862</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.078145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.488208</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.092035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.153925</td>\n",
       "      <td>0.017445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090870</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.071670</td>\n",
       "      <td>0.032670</td>\n",
       "      <td>0.078467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.059701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.550000e-08</td>\n",
       "      <td>6.070000e-08</td>\n",
       "      <td>0.003258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.999971</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014706</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>app_4</td>\n",
       "      <td>0</td>\n",
       "      <td>Hit-6+ Vintage</td>\n",
       "      <td>821.0</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>M</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>SELF-EMPLOYED</td>\n",
       "      <td>N</td>\n",
       "      <td>-11111</td>\n",
       "      <td>U</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>27</td>\n",
       "      <td>0.318103</td>\n",
       "      <td>141</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>0.318103</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>app_5</td>\n",
       "      <td>0</td>\n",
       "      <td>Hit-6+ Vintage</td>\n",
       "      <td>807.0</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>M</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-8888.000000</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-999</td>\n",
       "      <td>-8888</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>0.367035</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-999</td>\n",
       "      <td>-8888</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001179</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009460</td>\n",
       "      <td>0.004361</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015036</td>\n",
       "      <td>0.013894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.151776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.157996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.540541</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>app_6</td>\n",
       "      <td>0</td>\n",
       "      <td>Hit-6+ Vintage</td>\n",
       "      <td>788.0</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>Others</td>\n",
       "      <td>M</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>N</td>\n",
       "      <td>-11111</td>\n",
       "      <td>N</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-8888.000000</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>-9999</td>\n",
       "      <td>3</td>\n",
       "      <td>-9999</td>\n",
       "      <td>3</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-8888.000000</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>app_7</td>\n",
       "      <td>0</td>\n",
       "      <td>Hit-6+ Vintage</td>\n",
       "      <td>779.0</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>M</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>ATTORNEY AT LAW</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>U</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-8888</td>\n",
       "      <td>0.797378</td>\n",
       "      <td>17</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>0.740023</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.064677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.049645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013093</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005881</td>\n",
       "      <td>0.004618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003007</td>\n",
       "      <td>0.007886</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.360274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.992781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.995595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>app_8</td>\n",
       "      <td>0</td>\n",
       "      <td>Hit-6+ Vintage</td>\n",
       "      <td>801.0</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>Post-Graduate</td>\n",
       "      <td>M</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>SAL(RETIRAL AGE 60)</td>\n",
       "      <td>N</td>\n",
       "      <td>-11111</td>\n",
       "      <td>N</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-8888.000000</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-8888.000000</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>-8888</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>app_9</td>\n",
       "      <td>0</td>\n",
       "      <td>Hit-6+ Vintage</td>\n",
       "      <td>815.0</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>F</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>-11111</td>\n",
       "      <td>N</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>app_10</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>804.0</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>M</td>\n",
       "      <td>RESIDENT INDIAN</td>\n",
       "      <td>PROPRIETOR</td>\n",
       "      <td>N</td>\n",
       "      <td>-11111</td>\n",
       "      <td>N</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111.000000</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-999</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111.000000</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>-11111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  APP_ID_C  target          var_d1  var_d2           var_d3         var_d4  \\\n",
       "0    app_1       0  Hit-6+ Vintage   816.0  RESIDENT INDIAN  Post-Graduate   \n",
       "1    app_2       0             NaN   841.0  RESIDENT INDIAN  Post-Graduate   \n",
       "2    app_3       0  Hit-6+ Vintage   791.0  RESIDENT INDIAN  Post-Graduate   \n",
       "3    app_4       0  Hit-6+ Vintage   821.0  RESIDENT INDIAN       Graduate   \n",
       "4    app_5       0  Hit-6+ Vintage   807.0  RESIDENT INDIAN       Graduate   \n",
       "5    app_6       0  Hit-6+ Vintage   788.0  RESIDENT INDIAN         Others   \n",
       "6    app_7       0  Hit-6+ Vintage   779.0  RESIDENT INDIAN       Graduate   \n",
       "7    app_8       0  Hit-6+ Vintage   801.0  RESIDENT INDIAN  Post-Graduate   \n",
       "8    app_9       0  Hit-6+ Vintage   815.0  RESIDENT INDIAN       Graduate   \n",
       "9   app_10       0             NaN   804.0  RESIDENT INDIAN       Graduate   \n",
       "\n",
       "  var_d5           var_d6               var_d7 var_d8  var_d9 var_d11  var_b1  \\\n",
       "0      M  RESIDENT INDIAN        SELF-EMPLOYED      Y  -11111       N   -9999   \n",
       "1      F  RESIDENT INDIAN             SALARIED      N  -11111       N  -11111   \n",
       "2      M  RESIDENT INDIAN           PROPRIETOR      Y       1       N   -8888   \n",
       "3      M  RESIDENT INDIAN        SELF-EMPLOYED      N  -11111       U   -8888   \n",
       "4      M  RESIDENT INDIAN             SALARIED      Y       0       N   -9999   \n",
       "5      M  RESIDENT INDIAN             SALARIED      N  -11111       N   -9999   \n",
       "6      M  RESIDENT INDIAN      ATTORNEY AT LAW      Y       1       U   -8888   \n",
       "7      M  RESIDENT INDIAN  SAL(RETIRAL AGE 60)      N  -11111       N   -9999   \n",
       "8      F  RESIDENT INDIAN                  NaN      Y  -11111       N   -9999   \n",
       "9      M  RESIDENT INDIAN           PROPRIETOR      N  -11111       N  -11111   \n",
       "\n",
       "   var_b2  var_b3  var_b4  var_b5  var_b6  var_b7  var_b8  var_b9  \\\n",
       "0   -9999   -9999   -9999   -9999   -9999   -9999   -9999   -9999   \n",
       "1  -11111  -11111  -11111  -11111  -11111  -11111  -11111  -11111   \n",
       "2   -8888   -8888   -8888    -999    -999    -999    -999   -8888   \n",
       "3   -8888   -8888   -8888    -999    -999    -999    -999      27   \n",
       "4   -9999   -9999   -9999   -9999   -9999   -9999   -9999   -9999   \n",
       "5   -9999   -9999   -9999   -9999   -9999   -9999   -9999   -9999   \n",
       "6   -8888   -8888   -8888    -999    -999    -999    -999   -8888   \n",
       "7   -9999   -9999   -9999   -9999   -9999   -9999   -9999   -9999   \n",
       "8   -9999   -9999   -9999   -9999   -9999   -9999   -9999   -9999   \n",
       "9  -11111  -11111  -11111  -11111  -11111  -11111  -11111  -11111   \n",
       "\n",
       "        var_b10  var_b11  var_b12  var_b13  var_b14  var_b15  var_b16  \\\n",
       "0  -8888.000000    -9999    -8888    -8888    -8888    -8888        0   \n",
       "1 -11111.000000   -11111   -11111   -11111   -11111   -11111        1   \n",
       "2      0.824694        8    -8888    -8888    -8888    -8888        2   \n",
       "3      0.318103      141    -9999    -9999    -9999    -9999        5   \n",
       "4  -8888.000000    -9999    -8888     -999    -8888        1        2   \n",
       "5  -8888.000000    -9999    -9999    -9999    -9999    -9999        0   \n",
       "6      0.797378       17    -9999    -9999    -9999    -9999        1   \n",
       "7  -8888.000000    -9999    -8888    -8888    -8888    -8888        1   \n",
       "8  -9999.000000    -9999    -9999    -9999    -9999    -9999        0   \n",
       "9 -11111.000000   -11111   -11111   -11111   -11111   -11111   -11111   \n",
       "\n",
       "   var_b17  var_b18  var_b19  var_b20  var_b21  var_b22  var_b23  \\\n",
       "0        3    -9999    -9999     -999    -9999    -8888    -8888   \n",
       "1       12     -999        2        1     -999   -11111   -11111   \n",
       "2        9        1        3        1    -9999    -8888    -8888   \n",
       "3        8        1        2    -9999     -999    -8888    -8888   \n",
       "4       35        2        5        1     -999    -8888    -8888   \n",
       "5        5    -9999        3    -9999        3    -9999    -9999   \n",
       "6        4     -999    -9999    -9999    -9999    -8888    -8888   \n",
       "7        7        1        3        2    -9999    -9999    -9999   \n",
       "8        1    -9999    -9999    -9999    -9999    -9999    -9999   \n",
       "9     -999   -11111   -11111   -11111   -11111   -11111   -11111   \n",
       "\n",
       "        var_b24  var_b25  var_b26  var_b27  var_b28  var_l_1  var_l_2  \\\n",
       "0      0.632344    -8888    -8888    -8888    -8888        0        0   \n",
       "1 -11111.000000   -11111   -11111   -11111   -11111        0        0   \n",
       "2      0.824694    -8888    -8888    -8888    -8888        0        0   \n",
       "3      0.318103    -9999    -9999    -9999    -9999        0        0   \n",
       "4      0.367035    -8888     -999    -8888        1        0        0   \n",
       "5  -8888.000000    -8888    -8888    -8888    -8888        0        0   \n",
       "6      0.740023    -9999    -9999    -9999    -9999        0        0   \n",
       "7  -8888.000000    -8888    -8888    -8888    -8888        0        0   \n",
       "8  -9999.000000    -9999    -9999    -9999    -9999        0        0   \n",
       "9 -11111.000000   -11111   -11111   -11111   -11111        0        0   \n",
       "\n",
       "   var_l_3  var_l_4  var_l_5  var_l_6  var_l_7  var_l_8   var_l_9  var_l_10  \\\n",
       "0        0        0        0        0        0        0  0.000000       0.0   \n",
       "1        0        0        0        0        0        0  0.000000       0.0   \n",
       "2        0        0        0        0        0        0  0.000000       0.0   \n",
       "3        0        0        0        0        0        0  0.000000       0.0   \n",
       "4        0        0        0        0        0        0  0.001237       0.0   \n",
       "5        0        0        0        0        0        0  0.000000       0.0   \n",
       "6        0        0        0        0        0        0  0.000442       0.0   \n",
       "7        0        0        0        0        0        0  0.000000       0.0   \n",
       "8        0        0        0        0        0        0  0.000000       0.0   \n",
       "9        0        0        0        0        0        0  0.000000       0.0   \n",
       "\n",
       "   var_l_11  var_l_12  var_l_13  var_l_14  var_l_15  var_l_16  var_l_17  \\\n",
       "0  0.000000       0.0  0.000000       0.0  0.000000       0.0       0.0   \n",
       "1  0.000000       0.0  0.000000       0.0  0.000000       0.0       0.0   \n",
       "2  0.000000       0.0  0.000000       0.0  0.000000       0.0       0.0   \n",
       "3  0.000000       0.0  0.000000       0.0  0.000000       0.0       0.0   \n",
       "4  0.000069       0.0  0.021505       0.0  0.002809       0.0       0.0   \n",
       "5  0.000000       0.0  0.000000       0.0  0.000000       0.0       0.0   \n",
       "6  0.000756       0.0  0.003072       0.0  0.005618       0.0       0.0   \n",
       "7  0.000000       0.0  0.000000       0.0  0.000000       0.0       0.0   \n",
       "8  0.000000       0.0  0.000000       0.0  0.000000       0.0       0.0   \n",
       "9  0.000000       0.0  0.000000       0.0  0.000000       0.0       0.0   \n",
       "\n",
       "   var_l_18  var_l_19  var_l_20  var_l_21  var_l_22  var_l_23  var_l_24  \\\n",
       "0       0.0  0.000000  0.000000       0.0  0.000000       0.0  0.000000   \n",
       "1       0.0  0.000000  0.000000       0.0  0.000000       0.0  0.000000   \n",
       "2       0.0  0.019300  0.000026       0.0  0.000045       0.0  0.004975   \n",
       "3       0.0  0.000000  0.000000       0.0  0.000000       0.0  0.000000   \n",
       "4       0.0  0.000129  0.000000       0.0  0.000000       0.0  0.000000   \n",
       "5       0.0  0.000000  0.000000       0.0  0.000000       0.0  0.000000   \n",
       "6       0.0  0.000043  0.001551       0.0  0.001645       0.0  0.064677   \n",
       "7       0.0  0.000000  0.000000       0.0  0.000000       0.0  0.000000   \n",
       "8       0.0  0.000000  0.000000       0.0  0.000000       0.0  0.000000   \n",
       "9       0.0  0.000000  0.000000       0.0  0.000000       0.0  0.000000   \n",
       "\n",
       "   var_l_25  var_l_26  var_l_27  var_l_28  var_l_29  var_l_30  var_l_31  \\\n",
       "0       0.0     0.000       0.0  0.000000   0.00000       0.0       0.0   \n",
       "1       0.0     0.000       0.0  0.000000   0.00000       0.0       0.0   \n",
       "2       0.0     0.008       0.0  0.000775   0.00003       0.0       0.0   \n",
       "3       0.0     0.000       0.0  0.000000   0.00000       0.0       0.0   \n",
       "4       0.0     0.000       0.0  0.000000   0.00000       0.0       0.0   \n",
       "5       0.0     0.000       0.0  0.000000   0.00000       0.0       0.0   \n",
       "6       0.0     0.056       0.0  0.000000   0.00000       0.0       0.0   \n",
       "7       0.0     0.000       0.0  0.000000   0.00000       0.0       0.0   \n",
       "8       0.0     0.000       0.0  0.000000   0.00000       0.0       0.0   \n",
       "9       0.0     0.000       0.0  0.000000   0.00000       0.0       0.0   \n",
       "\n",
       "   var_l_32  var_l_33  var_l_34  var_l_35  var_l_36  var_l_37  var_l_38  \\\n",
       "0  0.000000  0.000000       0.0       0.0  0.000000       0.0  0.000000   \n",
       "1  0.000000  0.000000       0.0       0.0  0.000000       0.0  0.000000   \n",
       "2  0.003663  0.001085       0.0       0.0  0.001682       0.0  0.001256   \n",
       "3  0.000000  0.000000       0.0       0.0  0.000000       0.0  0.000000   \n",
       "4  0.000000  0.000000       0.0       0.0  0.000000       0.0  0.000000   \n",
       "5  0.000000  0.000000       0.0       0.0  0.000000       0.0  0.000000   \n",
       "6  0.000000  0.000000       0.0       0.0  0.001551       0.0  0.001645   \n",
       "7  0.000000  0.000000       0.0       0.0  0.000000       0.0  0.000000   \n",
       "8  0.000000  0.000000       0.0       0.0  0.000000       0.0  0.000000   \n",
       "9  0.000000  0.000000       0.0       0.0  0.000000       0.0  0.000000   \n",
       "\n",
       "   var_l_39  var_l_40  var_l_41  var_l_42  var_l_43  var_l_44  var_l_45  \\\n",
       "0       0.0  0.000000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "1       0.0  0.000000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "2       0.0  0.027027       0.0  0.021277       0.0  0.318331       0.0   \n",
       "3       0.0  0.000000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "4       0.0  0.000000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "5       0.0  0.000000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "6       0.0  0.058559       0.0  0.049645       0.0  0.013093       0.0   \n",
       "7       0.0  0.000000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "8       0.0  0.000000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "9       0.0  0.000000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "\n",
       "   var_l_46  var_l_47  var_l_48  var_l_49  var_l_50  var_l_51  var_l_52  \\\n",
       "0  0.000000  0.000000  0.000000       0.0  0.000000       0.0      0.00   \n",
       "1  0.000000  0.000000  0.000000       0.0  0.000000       0.0      0.00   \n",
       "2  0.013013  0.000202  0.002862       0.0  0.002368       0.0      0.12   \n",
       "3  0.000000  0.000000  0.000000       0.0  0.000000       0.0      0.00   \n",
       "4  0.015930  0.000000  0.000000       0.0  0.000000       0.0      0.00   \n",
       "5  0.000000  0.000000  0.000000       0.0  0.000000       0.0      0.00   \n",
       "6  0.001795  0.000000  0.000000       0.0  0.000000       0.0      0.00   \n",
       "7  0.000000  0.000000  0.000000       0.0  0.000000       0.0      0.00   \n",
       "8  0.000000  0.000000  0.000000       0.0  0.000000       0.0      0.00   \n",
       "9  0.000000  0.000000  0.000000       0.0  0.000000       0.0      0.00   \n",
       "\n",
       "   var_l_53  var_l_54  var_l_55  var_l_56  var_l_57  var_l_58  var_l_59  \\\n",
       "0       0.0   0.00000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "1       0.0   0.00000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "2       0.0   0.04712       0.0  0.052632       0.0  0.022995       0.0   \n",
       "3       0.0   0.00000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "4       0.0   0.00000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "5       0.0   0.00000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "6       0.0   0.00000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "7       0.0   0.00000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "8       0.0   0.00000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "9       0.0   0.00000       0.0  0.000000       0.0  0.000000       0.0   \n",
       "\n",
       "   var_l_60  var_l_61  var_l_62  var_l_63  var_l_64  var_l_65  var_l_66  \\\n",
       "0  0.000000       0.0         0         0  0.000000       0.0  0.000000   \n",
       "1  0.000000       0.0         0         0  0.000000       0.0  0.000000   \n",
       "2  0.078145       0.0         0         0  0.488208       0.0  0.092035   \n",
       "3  0.000000       0.0         0         0  0.000000       0.0  0.000000   \n",
       "4  0.000859       0.0         0         0  0.001179       0.0  0.000514   \n",
       "5  0.000000       0.0         0         0  0.000000       0.0  0.000000   \n",
       "6  0.000000       0.0         0         0  0.000000       0.0  0.000283   \n",
       "7  0.000000       0.0         0         0  0.000000       0.0  0.000000   \n",
       "8  0.000000       0.0         0         0  0.000000       0.0  0.000000   \n",
       "9  0.000000       0.0         0         0  0.000000       0.0  0.000000   \n",
       "\n",
       "   var_l_67  var_l_68  var_l_69  var_l_70  var_l_71  var_l_72  var_l_73  \\\n",
       "0       0.0  0.000000  0.000000       0.0  0.000000  0.000000  0.000000   \n",
       "1       0.0  0.000000  0.000000       0.0  0.000000  0.000000  0.000000   \n",
       "2       0.0  0.153925  0.017445       0.0  0.090870  0.000002  0.071670   \n",
       "3       0.0  0.000000  0.000000       0.0  0.000000  0.000000  0.000000   \n",
       "4       0.0  0.009460  0.004361       0.0  0.000494  0.000000  0.015036   \n",
       "5       0.0  0.000000  0.000000       0.0  0.000000  0.000000  0.000000   \n",
       "6       0.0  0.005881  0.004618       0.0  0.000273  0.000000  0.003007   \n",
       "7       0.0  0.000000  0.000000       0.0  0.000000  0.000000  0.000000   \n",
       "8       0.0  0.000000  0.000000       0.0  0.000000  0.000000  0.000000   \n",
       "9       0.0  0.000000  0.000000       0.0  0.000000  0.000000  0.000000   \n",
       "\n",
       "   var_l_74  var_l_75  var_l_76  var_l_77  var_l_78  var_l_79  var_l_80  \\\n",
       "0  0.000000  0.000000       0.0  0.000000       0.0       0.0       0.0   \n",
       "1  0.000000  0.000000       0.0  0.000000       0.0       0.0       0.0   \n",
       "2  0.032670  0.078467       0.0  0.059701       0.0       0.0       0.0   \n",
       "3  0.000000  0.000000       0.0  0.000000       0.0       0.0       0.0   \n",
       "4  0.013894  0.000000       0.0  0.000000       0.0       0.0       0.0   \n",
       "5  0.000000  0.000000       0.0  0.000000       0.0       0.0       0.0   \n",
       "6  0.007886  0.000000       0.0  0.000000       0.0       0.0       0.0   \n",
       "7  0.000000  0.000000       0.0  0.000000       0.0       0.0       0.0   \n",
       "8  0.000000  0.000000       0.0  0.000000       0.0       0.0       0.0   \n",
       "9  0.000000  0.000000       0.0  0.000000       0.0       0.0       0.0   \n",
       "\n",
       "   var_l_81  var_l_82  var_l_83  var_l_84  var_l_85  var_l_86  var_l_87  \\\n",
       "0         0         0         0         0  0.000000       0.0  0.000000   \n",
       "1         0         0         0         0  0.000000       0.0  0.000000   \n",
       "2         0         0         0         0  0.001555       0.0  0.009174   \n",
       "3         0         0         0         0  0.000000       0.0  0.000000   \n",
       "4         0         0         0         0  0.000000       0.0  0.000000   \n",
       "5         0         0         0         0  0.000000       0.0  0.000000   \n",
       "6         0         0         0         0  0.000000       0.0  0.000000   \n",
       "7         0         0         0         0  0.000000       0.0  0.000000   \n",
       "8         0         0         0         0  0.000000       0.0  0.000000   \n",
       "9         0         0         0         0  0.000000       0.0  0.000000   \n",
       "\n",
       "   var_l_88  var_l_89  var_l_90  var_l_91  var_l_92      var_l_93  \\\n",
       "0       0.0  0.000000       0.0  0.000000       0.0  0.000000e+00   \n",
       "1       0.0  0.000000       0.0  0.000000       0.0  0.000000e+00   \n",
       "2       0.0  0.000000       0.0  0.001511       0.0  4.550000e-08   \n",
       "3       0.0  0.000000       0.0  0.000000       0.0  0.000000e+00   \n",
       "4       0.0  0.008813       0.0  0.000000       0.0  0.000000e+00   \n",
       "5       0.0  0.000000       0.0  0.000000       0.0  0.000000e+00   \n",
       "6       0.0  0.000000       0.0  0.000000       0.0  0.000000e+00   \n",
       "7       0.0  0.000000       0.0  0.000000       0.0  0.000000e+00   \n",
       "8       0.0  0.000000       0.0  0.000000       0.0  0.000000e+00   \n",
       "9       0.0  0.000000       0.0  0.000000       0.0  0.000000e+00   \n",
       "\n",
       "       var_l_94  var_l_95  var_l_96  var_l_97  var_l_98  var_l_99  var_l_100  \\\n",
       "0  0.000000e+00  0.000000       0.0  0.000000       0.0  0.000000        0.0   \n",
       "1  0.000000e+00  0.000000       0.0  0.000000       0.0  0.000000        0.0   \n",
       "2  6.070000e-08  0.003258       0.0  0.004462       0.0  0.000000        0.0   \n",
       "3  0.000000e+00  0.000000       0.0  0.000000       0.0  0.000000        0.0   \n",
       "4  0.000000e+00  0.000000       0.0  0.000000       0.0  0.151776        0.0   \n",
       "5  0.000000e+00  0.000000       0.0  0.000000       0.0  0.000000        0.0   \n",
       "6  0.000000e+00  0.000000       0.0  0.000000       0.0  0.219700        0.0   \n",
       "7  0.000000e+00  0.000000       0.0  0.000000       0.0  0.000000        0.0   \n",
       "8  0.000000e+00  0.000000       0.0  0.000000       0.0  0.000000        0.0   \n",
       "9  0.000000e+00  0.000000       0.0  0.000000       0.0  0.000000        0.0   \n",
       "\n",
       "   var_l_101  var_l_102  var_l_103  var_l_104  var_l_105  var_l_106  \\\n",
       "0   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "1   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "2   0.000000        0.0   0.000051        0.0   0.000166        0.0   \n",
       "3   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "4   0.157996        0.0   0.000000        0.0   0.000000        0.0   \n",
       "5   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "6   0.360274        0.0   0.992781        0.0   0.995595        0.0   \n",
       "7   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "8   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "9   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "\n",
       "   var_l_107  var_l_108  var_l_109  var_l_110  var_l_111  var_l_112  \\\n",
       "0   0.000000   0.000000        0.0        0.0   0.000000        0.0   \n",
       "1   0.000000   0.000000        0.0        0.0   0.000000        0.0   \n",
       "2   0.001833   0.999971        0.0        0.0   0.014706        0.0   \n",
       "3   0.000000   0.000000        0.0        0.0   0.000000        0.0   \n",
       "4   0.000000   0.000000        0.0        0.0   0.000000        0.0   \n",
       "5   0.000000   0.000000        0.0        0.0   0.000000        0.0   \n",
       "6   0.000000   0.000000        0.0        0.0   0.722222        0.0   \n",
       "7   0.000000   0.000000        0.0        0.0   0.000000        0.0   \n",
       "8   0.000000   0.000000        0.0        0.0   0.000000        0.0   \n",
       "9   0.000000   0.000000        0.0        0.0   0.000000        0.0   \n",
       "\n",
       "   var_l_113  var_l_114  var_l_115  var_l_116  var_l_117  var_l_118  \\\n",
       "0   0.000000        0.0   0.000000        0.0        0.0        0.0   \n",
       "1   0.000000        0.0   0.000000        0.0        0.0        0.0   \n",
       "2   0.033333        0.0   0.011494        0.5        0.0        0.0   \n",
       "3   0.000000        0.0   0.000000        0.0        0.0        0.0   \n",
       "4   0.000000        0.0   0.000000        0.0        0.0        0.0   \n",
       "5   0.000000        0.0   0.000000        0.0        0.0        0.0   \n",
       "6   0.777778        0.0   0.000000        0.0        0.0        0.0   \n",
       "7   0.000000        0.0   0.000000        0.0        0.0        0.0   \n",
       "8   0.000000        0.0   0.000000        0.0        0.0        0.0   \n",
       "9   0.000000        0.0   0.000000        0.0        0.0        0.0   \n",
       "\n",
       "   var_l_119  var_l_120  var_l_121  var_l_122  var_l_123  var_l_124  \\\n",
       "0   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "1   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "2   0.088235        0.0   0.100000        0.0   0.011494        0.5   \n",
       "3   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "4   0.000000        0.0   0.000000        0.0   0.540541        0.0   \n",
       "5   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "6   0.722222        0.0   0.777778        0.0   0.380952        0.0   \n",
       "7   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "8   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "9   0.000000        0.0   0.000000        0.0   0.000000        0.0   \n",
       "\n",
       "   var_l_125  var_l_126   type  apply_time  \n",
       "0   0.000000        0.0  train  2019-03-01  \n",
       "1   0.000000        0.0  train  2019-03-01  \n",
       "2   0.000000        0.0  train  2019-03-01  \n",
       "3   0.000000        0.0  train  2019-03-01  \n",
       "4   0.285714        0.0  train  2019-03-01  \n",
       "5   0.000000        0.0  train  2019-03-01  \n",
       "6   0.571429        0.0  train  2019-03-01  \n",
       "7   0.000000        0.0  train  2019-03-01  \n",
       "8   0.000000        0.0  train  2019-03-01  \n",
       "9   0.000000        0.0  train  2019-03-01  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('/Users/ryanzheng/PycharmProjects/autobmt/examples/example_data/tutorial_data.csv')\n",
    "print('Shape:',data.shape)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据包含2019年5月 - 7月的数据。其中将用3月和4月数据用于训练样本，5月、6月、7月数据作为时间外样本（OOT）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['month'] = data['apply_time'].map(lambda x: x[:7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month: ['2019-03' '2019-04' '2019-05' '2019-06' '2019-07']\n"
     ]
    }
   ],
   "source": [
    "print('month:',data.month.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train size: (43576, 169) \n",
      "OOT size: (65364, 169)\n"
     ]
    }
   ],
   "source": [
    "train = data.loc[data.month.isin(['2019-03','2019-04'])==True,:]\n",
    "OOT = data.loc[data.month.isin(['2019-03','2019-04'])==False,:]\n",
    "\n",
    "print('train size:',train.shape,'\\nOOT size:',OOT.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. EDA"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "2.1 autobmt.detect(dataframe):"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "用于检测数据情况（EDA）。输出每列特征的统计性信息和其他信息，主要的信息包括：特征类型、缺失值、unique values、某取值的最大占比、数值变量的平均值；离散值变量的众数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var_name</th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>missing</th>\n",
       "      <th>missing_q</th>\n",
       "      <th>unique</th>\n",
       "      <th>value_max_percent</th>\n",
       "      <th>mean_or_top1</th>\n",
       "      <th>std_or_top2</th>\n",
       "      <th>min_or_top3</th>\n",
       "      <th>1%_or_top4</th>\n",
       "      <th>10%_or_top5</th>\n",
       "      <th>50%_or_bottom5</th>\n",
       "      <th>75%_or_bottom4</th>\n",
       "      <th>90%_or_bottom3</th>\n",
       "      <th>99%_or_bottom2</th>\n",
       "      <th>max_or_bottom1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>APP_ID_C</td>\n",
       "      <td>object</td>\n",
       "      <td>43576</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43576</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>app_659:0.00%</td>\n",
       "      <td>app_9328:0.00%</td>\n",
       "      <td>app_3714:0.00%</td>\n",
       "      <td>app_11472:0.00%</td>\n",
       "      <td>app_4850:0.00%</td>\n",
       "      <td>app_8200:0.00%</td>\n",
       "      <td>app_1631:0.00%</td>\n",
       "      <td>app_6405:0.00%</td>\n",
       "      <td>app_10920:0.00%</td>\n",
       "      <td>app_25409:0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>target</td>\n",
       "      <td>int64</td>\n",
       "      <td>43576</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.978681</td>\n",
       "      <td>0.021319</td>\n",
       "      <td>0.144447</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>var_d1</td>\n",
       "      <td>object</td>\n",
       "      <td>43576</td>\n",
       "      <td>37.57%</td>\n",
       "      <td>0.375734</td>\n",
       "      <td>2</td>\n",
       "      <td>0.603245</td>\n",
       "      <td>Hit-6+ Vintage:60.32%</td>\n",
       "      <td>Hit-lt 6 Vinta:2.10%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Hit-6+ Vintage:60.32%</td>\n",
       "      <td>Hit-lt 6 Vinta:2.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>var_d2</td>\n",
       "      <td>float64</td>\n",
       "      <td>43576</td>\n",
       "      <td>5.44%</td>\n",
       "      <td>0.054434</td>\n",
       "      <td>389</td>\n",
       "      <td>0.249220</td>\n",
       "      <td>570.491578</td>\n",
       "      <td>355.565339</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>864.0</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>var_d3</td>\n",
       "      <td>object</td>\n",
       "      <td>43576</td>\n",
       "      <td>5.31%</td>\n",
       "      <td>0.053103</td>\n",
       "      <td>6</td>\n",
       "      <td>0.940013</td>\n",
       "      <td>RESIDENT INDIAN:94.00%</td>\n",
       "      <td>NON-RESIDENT INDIAN:0.64%</td>\n",
       "      <td>PRIVATE LTD COMPANIES:0.02%</td>\n",
       "      <td>PARTNERSHIP FIRM:0.02%</td>\n",
       "      <td>PUBLIC LTD COMPANIES:0.00%</td>\n",
       "      <td>NON-RESIDENT INDIAN:0.64%</td>\n",
       "      <td>PRIVATE LTD COMPANIES:0.02%</td>\n",
       "      <td>PARTNERSHIP FIRM:0.02%</td>\n",
       "      <td>PUBLIC LTD COMPANIES:0.00%</td>\n",
       "      <td>OVERSEAS CITIZEN OF INDIA:0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>var_d4</td>\n",
       "      <td>object</td>\n",
       "      <td>43576</td>\n",
       "      <td>1.08%</td>\n",
       "      <td>0.010786</td>\n",
       "      <td>5</td>\n",
       "      <td>0.553011</td>\n",
       "      <td>Graduate:55.30%</td>\n",
       "      <td>Post-Graduate:21.57%</td>\n",
       "      <td>Others:10.71%</td>\n",
       "      <td>Under Graduate:10.67%</td>\n",
       "      <td>Professional:0.67%</td>\n",
       "      <td>Graduate:55.30%</td>\n",
       "      <td>Post-Graduate:21.57%</td>\n",
       "      <td>Others:10.71%</td>\n",
       "      <td>Under Graduate:10.67%</td>\n",
       "      <td>Professional:0.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>var_d5</td>\n",
       "      <td>object</td>\n",
       "      <td>43576</td>\n",
       "      <td>1.08%</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>3</td>\n",
       "      <td>0.796998</td>\n",
       "      <td>M:79.70%</td>\n",
       "      <td>F:14.33%</td>\n",
       "      <td>O:4.89%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>M:79.70%</td>\n",
       "      <td>F:14.33%</td>\n",
       "      <td>O:4.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>var_d6</td>\n",
       "      <td>object</td>\n",
       "      <td>43576</td>\n",
       "      <td>1.08%</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>13</td>\n",
       "      <td>0.933427</td>\n",
       "      <td>RESIDENT INDIAN:93.34%</td>\n",
       "      <td>PRIVATE LTD COMPANIES:2.57%</td>\n",
       "      <td>PARTNERSHIP FIRM:1.45%</td>\n",
       "      <td>PUBLIC LTD COMPANIES:0.73%</td>\n",
       "      <td>NON-RESIDENT INDIAN:0.64%</td>\n",
       "      <td>CO-OPERATIVE SOCIETIES:0.01%</td>\n",
       "      <td>LIMITED LIABILITY PARTNERSHIP:0.00%</td>\n",
       "      <td>ASSOCIATION:0.00%</td>\n",
       "      <td>TRUST-NGO:0.00%</td>\n",
       "      <td>OVERSEAS CITIZEN OF INDIA:0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>var_d7</td>\n",
       "      <td>object</td>\n",
       "      <td>43576</td>\n",
       "      <td>1.60%</td>\n",
       "      <td>0.016018</td>\n",
       "      <td>84</td>\n",
       "      <td>0.314256</td>\n",
       "      <td>SALARIED:31.43%</td>\n",
       "      <td>PROPRIETOR:31.31%</td>\n",
       "      <td>SELF-EMPLOYED:10.74%</td>\n",
       "      <td>OTHERS:6.40%</td>\n",
       "      <td>FIRST TIME USERS:2.72%</td>\n",
       "      <td>INDUSTRY:0.00%</td>\n",
       "      <td>Trading:0.00%</td>\n",
       "      <td>TYPIST:0.00%</td>\n",
       "      <td>NURSE:0.00%</td>\n",
       "      <td>TAXI DRIVER:0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>var_d8</td>\n",
       "      <td>object</td>\n",
       "      <td>43576</td>\n",
       "      <td>1.08%</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>2</td>\n",
       "      <td>0.598977</td>\n",
       "      <td>Y:59.90%</td>\n",
       "      <td>N:39.03%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Y:59.90%</td>\n",
       "      <td>N:39.03%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var_name     type   size missing  missing_q  unique  value_max_percent  \\\n",
       "0  APP_ID_C   object  43576   0.00%   0.000000   43576           0.000023   \n",
       "1    target    int64  43576   0.00%   0.000000       2           0.978681   \n",
       "2    var_d1   object  43576  37.57%   0.375734       2           0.603245   \n",
       "3    var_d2  float64  43576   5.44%   0.054434     389           0.249220   \n",
       "4    var_d3   object  43576   5.31%   0.053103       6           0.940013   \n",
       "5    var_d4   object  43576   1.08%   0.010786       5           0.553011   \n",
       "6    var_d5   object  43576   1.08%   0.010763       3           0.796998   \n",
       "7    var_d6   object  43576   1.08%   0.010763      13           0.933427   \n",
       "8    var_d7   object  43576   1.60%   0.016018      84           0.314256   \n",
       "9    var_d8   object  43576   1.08%   0.010763       2           0.598977   \n",
       "\n",
       "             mean_or_top1                  std_or_top2  \\\n",
       "0           app_659:0.00%               app_9328:0.00%   \n",
       "1                0.021319                     0.144447   \n",
       "2   Hit-6+ Vintage:60.32%         Hit-lt 6 Vinta:2.10%   \n",
       "3              570.491578                   355.565339   \n",
       "4  RESIDENT INDIAN:94.00%    NON-RESIDENT INDIAN:0.64%   \n",
       "5         Graduate:55.30%         Post-Graduate:21.57%   \n",
       "6                M:79.70%                     F:14.33%   \n",
       "7  RESIDENT INDIAN:93.34%  PRIVATE LTD COMPANIES:2.57%   \n",
       "8         SALARIED:31.43%            PROPRIETOR:31.31%   \n",
       "9                Y:59.90%                     N:39.03%   \n",
       "\n",
       "                   min_or_top3                  1%_or_top4  \\\n",
       "0               app_3714:0.00%             app_11472:0.00%   \n",
       "1                          0.0                         0.0   \n",
       "2                         None                        None   \n",
       "3                         -1.0                        -1.0   \n",
       "4  PRIVATE LTD COMPANIES:0.02%      PARTNERSHIP FIRM:0.02%   \n",
       "5                Others:10.71%       Under Graduate:10.67%   \n",
       "6                      O:4.89%                        None   \n",
       "7       PARTNERSHIP FIRM:1.45%  PUBLIC LTD COMPANIES:0.73%   \n",
       "8         SELF-EMPLOYED:10.74%                OTHERS:6.40%   \n",
       "9                         None                        None   \n",
       "\n",
       "                  10%_or_top5                50%_or_bottom5  \\\n",
       "0              app_4850:0.00%                app_8200:0.00%   \n",
       "1                         0.0                           0.0   \n",
       "2                        None                          None   \n",
       "3                        -1.0                         778.0   \n",
       "4  PUBLIC LTD COMPANIES:0.00%     NON-RESIDENT INDIAN:0.64%   \n",
       "5          Professional:0.67%               Graduate:55.30%   \n",
       "6                        None                          None   \n",
       "7   NON-RESIDENT INDIAN:0.64%  CO-OPERATIVE SOCIETIES:0.01%   \n",
       "8      FIRST TIME USERS:2.72%                INDUSTRY:0.00%   \n",
       "9                        None                          None   \n",
       "\n",
       "                        75%_or_bottom4          90%_or_bottom3  \\\n",
       "0                       app_1631:0.00%          app_6405:0.00%   \n",
       "1                                  0.0                     0.0   \n",
       "2                                 None                    None   \n",
       "3                                810.0                   832.0   \n",
       "4          PRIVATE LTD COMPANIES:0.02%  PARTNERSHIP FIRM:0.02%   \n",
       "5                 Post-Graduate:21.57%           Others:10.71%   \n",
       "6                                 None                M:79.70%   \n",
       "7  LIMITED LIABILITY PARTNERSHIP:0.00%       ASSOCIATION:0.00%   \n",
       "8                        Trading:0.00%            TYPIST:0.00%   \n",
       "9                                 None                    None   \n",
       "\n",
       "               99%_or_bottom2                   max_or_bottom1  \n",
       "0             app_10920:0.00%                  app_25409:0.00%  \n",
       "1                         1.0                              1.0  \n",
       "2       Hit-6+ Vintage:60.32%             Hit-lt 6 Vinta:2.10%  \n",
       "3                       864.0                            900.0  \n",
       "4  PUBLIC LTD COMPANIES:0.00%  OVERSEAS CITIZEN OF INDIA:0.00%  \n",
       "5       Under Graduate:10.67%               Professional:0.67%  \n",
       "6                    F:14.33%                          O:4.89%  \n",
       "7             TRUST-NGO:0.00%  OVERSEAS CITIZEN OF INDIA:0.00%  \n",
       "8                 NURSE:0.00%                TAXI DRIVER:0.00%  \n",
       "9                    Y:59.90%                         N:39.03%  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autobmt.detect(train)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 高效分箱并进行特征筛选"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. autobmt.selection.select(dataframe, target=’target’, empty=0.9, iv=0.02, corr=0.7, return_drop=False, exclude=None):"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "3.1 autobmt.FeatureSelection(dataframe,target='target',data_type='type',exclude_columns=['key', 'target', 'apply_time', 'type'],params=None, match_dict=None)\n",
    "\n",
    "可以根据特征缺失值占比，特征集中度，特征psi，特征iv值，特征iv差值，和高相关性6种方法进行变量筛选\n",
    "参数说明：\n",
    "dataframe (DataFrame): 需要进行变量筛选的数据集\n",
    "target (str): 目标值y变量名称\n",
    "data_type (str): 数据集划分标示的名称【即划分train、test、oot的字段名称】\n",
    "exclude_columns (list): 需要排除的特征，明确不被删去的列名，输入为list格式\n",
    "match_dict (DataFrame): 数据源特征字典\n",
    "params (dict): 筛选特征的方法字典，有'empty'、'const'、'psi'、'iv'、'iv_diff'、'corr' 6种筛选方法供选择。字典形如：\n",
    "{\n",
    "    'empty': {'threshold': 0.9},    #若特征的缺失值大于0.9被删除\n",
    "    'const': {'threshold': 0.95},   #若特征单个值的占比大于0.95被删除\n",
    "    'psi': {'threshold': 0.05},  #若特征在train、test上psi值大于0.05被删除\n",
    "    'iv': {'threshold': 0.02},  #若特征的iv值小于0.02被删除\n",
    "    'iv_diff': {'threshold': 2},    #若特征在train、test上的iv差值乘10后，大于2，特征被删除\n",
    "    'corr': {'threshold': 0.7}, #若两个特征相关性高于0.7时，iv值低的特征被删除\n",
    "}\n",
    "\n",
    "\n",
    "ps：没有指定params参数的话，默认是3种方法进行特征选择，默认的params：\n",
    "params = {'empty': {'threshold': 0.9},\n",
    "          'iv': {'threshold': 0.02},\n",
    "          'corr': {'threshold': 0.7},\n",
    "          }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-19 15:45:38,107 - autobmt.feature_selection[line:759] - INFO: 开始进行前置检查\n",
      "2023-11-19 15:45:38,109 - autobmt.feature_selection[line:764] - INFO: train、test数据集标识的字段名不存在！或未进行数据集的划分，筛选变量无法使用psi、iv_diff进行筛选，建议请将数据集划分为train、test!!!\n",
      "2023-11-19 15:45:38,269 - autobmt.feature_selection[line:787] - INFO: 数值特征个数: 156\n",
      "2023-11-19 15:45:38,270 - autobmt.feature_selection[line:788] - INFO: 字符特征个数: 8\n",
      "2023-11-19 15:45:38,270 - autobmt.feature_selection[line:789] - INFO: 日期特征个数: 0\n",
      "2023-11-19 15:45:38,271 - autobmt.feature_selection[line:791] - INFO: 数据集中包含有8个字符特征,0个日期特征\n",
      "2023-11-19 15:45:38,274 - autobmt.feature_selection[line:691] - INFO: 开始执行特征选择模块... 数据集结构为[(43576, 169)]\n",
      "2023-11-19 15:45:38,275 - autobmt.feature_selection[line:704] - INFO: 未指定筛选方法的阈值，使用默认方法和阈值：{'empty': {'threshold': 0.9}, 'iv': {'threshold': 0.02}, 'corr': {'threshold': 0.7}}\n",
      "2023-11-19 15:45:38,329 - autobmt.feature_selection[line:730] - INFO: 删除变量 ：[]\n",
      "2023-11-19 15:45:38,354 - autobmt.feature_selection[line:742] - INFO: empty 方法剔除变量，阈值为empty_selection_feature_flag (>0.9)，剔除的变量有 : 0 个\n",
      "2023-11-19 15:45:43,722 - autobmt.feature_selection[line:730] - INFO: 删除变量 ：['var_d9' 'var_l_1' 'var_l_2' 'var_l_3' 'var_l_4' 'var_l_5' 'var_l_6'\n",
      " 'var_l_7' 'var_l_8' 'var_l_10' 'var_l_12' 'var_l_14' 'var_l_15'\n",
      " 'var_l_16' 'var_l_18' 'var_l_21' 'var_l_23' 'var_l_24' 'var_l_25'\n",
      " 'var_l_26' 'var_l_27' 'var_l_28' 'var_l_29' 'var_l_30' 'var_l_31'\n",
      " 'var_l_32' 'var_l_33' 'var_l_34' 'var_l_35' 'var_l_37' 'var_l_39'\n",
      " 'var_l_41' 'var_l_42' 'var_l_43' 'var_l_45' 'var_l_47' 'var_l_49'\n",
      " 'var_l_51' 'var_l_53' 'var_l_55' 'var_l_57' 'var_l_59' 'var_l_61'\n",
      " 'var_l_62' 'var_l_63' 'var_l_65' 'var_l_67' 'var_l_70' 'var_l_72'\n",
      " 'var_l_75' 'var_l_76' 'var_l_77' 'var_l_78' 'var_l_79' 'var_l_80'\n",
      " 'var_l_81' 'var_l_82' 'var_l_83' 'var_l_84' 'var_l_85' 'var_l_86'\n",
      " 'var_l_87' 'var_l_88' 'var_l_90' 'var_l_92' 'var_l_93' 'var_l_94'\n",
      " 'var_l_95' 'var_l_96' 'var_l_97' 'var_l_98' 'var_l_100' 'var_l_102'\n",
      " 'var_l_104' 'var_l_106' 'var_l_107' 'var_l_108' 'var_l_109' 'var_l_110'\n",
      " 'var_l_112' 'var_l_114' 'var_l_115' 'var_l_116' 'var_l_117' 'var_l_118'\n",
      " 'var_l_120' 'var_l_122' 'var_l_124' 'var_l_126']\n",
      "2023-11-19 15:45:43,748 - autobmt.feature_selection[line:742] - INFO: iv 方法剔除变量，阈值为iv_selection_feature_flag (<0.02)，剔除的变量有 : 89 个\n",
      "2023-11-19 15:45:44,139 - autobmt.feature_selection[line:730] - INFO: 删除变量 ：['var_l_125', 'var_l_105', 'var_l_121', 'var_l_103', 'var_l_113', 'var_l_48', 'var_l_56', 'var_b4', 'var_l_101', 'var_b6', 'var_l_69', 'var_l_73', 'var_b10', 'var_b25', 'var_b7', 'var_b5', 'var_b26', 'var_l_54', 'var_l_22', 'var_l_74', 'var_l_111', 'var_l_99', 'var_b12', 'var_b23', 'var_l_13', 'var_b13', 'var_l_11', 'var_l_71', 'var_b28', 'var_b16', 'var_b2', 'var_l_36', 'var_b8', 'var_b14', 'var_l_44', 'var_l_38', 'var_l_46', 'var_b1', 'var_b27', 'var_l_17', 'var_b9', 'var_b3']\n",
      "2023-11-19 15:45:44,150 - autobmt.feature_selection[line:742] - INFO: corr 方法剔除变量，阈值为corr_selection_feature_flag (>0.7)，剔除的变量有 : 42 个\n",
      "2023-11-19 15:45:44,153 - autobmt.feature_selection[line:748] - INFO: 特征选择执行完成... 数据集结构为[(43576, 38)]\n"
     ]
    }
   ],
   "source": [
    "exclude_cols = ['APP_ID_C','month','type','apply_time'] #去掉其它列\n",
    "fs = autobmt.FeatureSelection(df=train, target='target',data_type='none', exclude_columns=exclude_cols)\n",
    "df_selected, selected_features, select_log_df, fbfb = fs.select()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保留的特征个数： 33\n",
      "保留的特征： ['var_d1', 'var_d2', 'var_d3', 'var_d4', 'var_d5', 'var_d6', 'var_d7', 'var_d8', 'var_d11', 'var_b11', 'var_b15', 'var_b17', 'var_b18', 'var_b19', 'var_b20', 'var_b21', 'var_b22', 'var_b24', 'var_l_9', 'var_l_19', 'var_l_20', 'var_l_40', 'var_l_50', 'var_l_52', 'var_l_58', 'var_l_60', 'var_l_64', 'var_l_66', 'var_l_68', 'var_l_89', 'var_l_91', 'var_l_119', 'var_l_123']\n",
      "筛选特征后的数据集： (43576, 38)\n",
      "筛选过程日志：        feature cn  miss_rate  empty_selection_feature_flag (>0.9)        IV  \\\n",
      "0       var_d1      0.375734                                    0  0.028056   \n",
      "1       var_d2      0.054434                                    0  0.366359   \n",
      "2       var_d3      0.053103                                    0  0.071922   \n",
      "3       var_d4      0.010786                                    0  0.034812   \n",
      "4       var_d5      0.010763                                    0  0.067966   \n",
      "..         ... ..        ...                                  ...       ...   \n",
      "159  var_l_122      0.000000                                    0  0.000000   \n",
      "160  var_l_123      0.000000                                    0  0.160748   \n",
      "161  var_l_124      0.000000                                    0  0.000000   \n",
      "162  var_l_125      0.000000                                    0  0.152557   \n",
      "163  var_l_126      0.000000                                    0  0.000000   \n",
      "\n",
      "     iv_selection_feature_flag (<0.02)  corr_selection_feature_flag (>0.7)  \\\n",
      "0                                    0                                   0   \n",
      "1                                    0                                   0   \n",
      "2                                    0                                   0   \n",
      "3                                    0                                   0   \n",
      "4                                    0                                   0   \n",
      "..                                 ...                                 ...   \n",
      "159                                  1                                   0   \n",
      "160                                  0                                   0   \n",
      "161                                  1                                   0   \n",
      "162                                  0                                   1   \n",
      "163                                  1                                   0   \n",
      "\n",
      "     feature_filter_flag  \n",
      "0                      0  \n",
      "1                      0  \n",
      "2                      0  \n",
      "3                      0  \n",
      "4                      0  \n",
      "..                   ...  \n",
      "159                    1  \n",
      "160                    0  \n",
      "161                    1  \n",
      "162                    1  \n",
      "163                    1  \n",
      "\n",
      "[164 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "print('保留的特征个数：',len(selected_features))\n",
    "print('保留的特征：',selected_features)\n",
    "print('筛选特征后的数据集：',df_selected.shape)\n",
    "print('筛选过程日志：',select_log_df)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "将筛选日志持久化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "select_log_df.to_csv('tutorial_code_select_log_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2 分箱"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "autobmt的分箱功能支持数值型数据和离散型分箱，默认分箱方法使用：卡方分箱。\n",
    "分箱方法的默认参数：\n",
    "min_sample_rate (number) : 每个箱子的最小占比，默认0.05\n",
    "n_bins (int): 需要分成几箱，默认10\n",
    "is_need_monotonic (bool) : 是否强制单调，默认True，强制单调\n",
    "is_empty_bin (bool) : 是否将空箱单独归为一个箱子，默认True，空箱单独归1箱\n",
    "\n",
    "\n",
    "autobmt.transformer.FeatureBin 是用来分箱的类，分箱步骤如下：\n",
    "\n",
    "1、* 初始化*: fb = autobmt.transformer.FeatureBin()\n",
    "2、*训练分箱*: fb.fit(dataframe, y = 'target', method = 'chi', , min_sample_rate = 0.05, n_bins = 10, is_need_monotonic=True, is_empty_bin=True)\n",
    "    y: 目标列\n",
    "    method: 分箱方法，支持'chi' (卡方分箱), 'dt' (决策树分箱),'kmeans' (聚类分箱), 'equal_freq' (等频分箱)\n",
    "    min_sample_rate: 每箱至少包含样本量，可以是数字或者占比\n",
    "    n_bins: 箱数，若无法分出这么多箱数，则会分出最多的箱数\n",
    "    is_need_monotonic: 是否强制单调，默认True，强制单调\n",
    "    is_empty_bin: 是否将空箱单独归为一个箱子，默认True，空箱单独归1箱\n",
    "3、*查看分箱节点*：fb.export()\n",
    "4、*手动调整分箱*: fb.manual_bin(dict)\n",
    "5、*应用分箱结果*: fb.transform(dataframe, labels=False):\n",
    "    labels: 是否将分箱结果转化成箱标签。False时输出0,1,2…（离散变量根据占比高低排序），True输出0.[-inf ~ 3.5), 1.[3.5 ~ inf), 2.nan。\n",
    "注意：1. 注意删去不需要分箱的列名，即非x的列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var_d2: ['0.[-inf ~ 747.0)', '1.[747.0 ~ 782.0)', '2.[782.0 ~ 820.0)', '3.[820.0 ~ inf)', '4.nan']\n",
      "var_d5: ['0.O,F', '1.M', '2.nan']\n",
      "var_d6: ['0.PUBLIC LTD COMPANIES,NON-RESIDENT INDIAN,PRIVATE LTD COMPANIES,PARTNERSHIP FIRM', '1.RESIDENT INDIAN,TRUST,TRUST-CLUBS/ASSN/SOC/SEC-25 CO.,HINDU UNDIVIDED FAMILY,CO-OPERATIVE SOCIETIES,LIMITED LIABILITY PARTNERSHIP,ASSOCIATION,OVERSEAS CITIZEN OF INDIA,TRUST-NGO', '2.nan']\n",
      "===========分割点===========\n",
      "var_d2: [747. 782. 820.  nan]\n",
      "var_d5: [list(['O', 'F']) list(['M']) list(['nan'])]\n",
      "var_d6: [list(['PUBLIC LTD COMPANIES', 'NON-RESIDENT INDIAN', 'PRIVATE LTD COMPANIES', 'PARTNERSHIP FIRM'])\n",
      " list(['RESIDENT INDIAN', 'TRUST', 'TRUST-CLUBS/ASSN/SOC/SEC-25 CO.', 'HINDU UNDIVIDED FAMILY', 'CO-OPERATIVE SOCIETIES', 'LIMITED LIABILITY PARTNERSHIP', 'ASSOCIATION', 'OVERSEAS CITIZEN OF INDIA', 'TRUST-NGO'])\n",
      " list(['nan'])]\n",
      "CPU times: user 165 ms, sys: 129 ms, total: 293 ms\n",
      "Wall time: 10.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 初始化\n",
    "fb = autobmt.transformer.FeatureBin()\n",
    "\n",
    "# 使用特征筛选后的数据进行训练：使用稳定的卡方分箱，规定每箱至少有5%数据, 空值将自动被归到为1箱。\n",
    "fb.fit(df_selected.drop(exclude_cols, axis=1), y = 'target', method = 'chi', min_sample_rate = 0.05, is_need_monotonic=False)\n",
    "\n",
    "# 仅展示部分分箱\n",
    "print('var_d2:',fb.export()['var_d2'])\n",
    "print('var_d5:',fb.export()['var_d5'])\n",
    "print('var_d6:',fb.export()['var_d6'])\n",
    "\n",
    "print('===========分割点===========')\n",
    "# 仅展示部分分箱\n",
    "print('var_d2:',fb.splits_dict['var_d2'])\n",
    "print('var_d5:',fb.splits_dict['var_d5'])\n",
    "print('var_d6:',fb.splits_dict['var_d6'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.3 观察并手动调整分箱：fb.manual_bin(dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3.1 观察分箱"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "plot_var_bin_summary(frame, cols, target='target', by='type', file_path=None, worksheet='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from autobmt.plot import plot_var_bin_summary\n",
    "\n",
    "# 看'var_d5'在时间内的分箱\n",
    "col = 'var_d5'\n",
    "\n",
    "plot_var_bin_summary(fb.transform(df_selected[[col,'target']], labels=True), cols=col)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3.2 调整分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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TknTjtfV07x03SJL6Dh2rffsPhZwbsh5rbZwk5y+15saYukoeTEu+PaqzFGvtIklrHM2PGGPcf8mm9ITj9RFJP7udGA3+Fc5/OJrbGGMKh9lFJyUPpk9JGhXGdT/JFyYH+l+YY8oYU0lSY0fztEiCab/2jtf7JY2JsK+oMMacL6mbo/njdAbTxZU80JekSQTTABAdhNMAAADAOex0Gd2EY5F9/lTxypYy5eqrfZfuknzljx9+7n1VvLKlYitdr5J1mqp5x26avWBZqv1s37lHvQeNVstOr+iC+vcq7wU3KrbS9Spb9041e/BFDR87WadOnQp6/ZSZf8uUqy9Trr6mzPxbkjRi3GTdcM9TKl77duWu3EjVGtyn59/pzYf06fBTwP6oHYLs8ZwtWzY90MK31eGBg0f0p//PI6tasGRVUmnwW6+/Muh5hQrm15WX1pQk/fznbMXFp/5558YtO/TaR/0l+YMtl4ApK8uIe8VaqzGTpkuSqlc5T1e6BOCSdGXdWkkP4oyZNF3OCnGr12/SijUbJEl3N22kPLlzufYT+GDCjz+nDKfDUat6paQSy+s2Rl7hNLAEesKx4xH3g8h1fvkTHTkar3atbtV1V10S1jW9Bo6WJBUskE+Pt28R0bjzl6yU5NtT+oJK5YOe17jhFZKkEydOauxv0yMaC1mCs4RwHknfO9pOKZ3htDHmcWOMdXy1TE+fYXrL8bqKpJdCXWSMqS/pIUfzZ9bakP8RaIyp5fJee0U434JKuYrYbcwqkl5xNA+11q4Nda21do+kzx3Njxpj6oUxbjZJXyllBuDsLyzGmEskXexoHpqOoDvdjDElJU1Q8jL3SyV9kM6u20hy/scbJb0BIEoIpwEAAIBz1Kp1m7ToH9+ClOpVzkt3fz/+PFWXNn5QX38/Thu37NDx4ye0a89+/TRpmurf1VnDx052vS4xMVHl6t2lzi9/olETp2jthi2Ki0/Q8eMntG3nHo39dbru/d/ratymq44cDb0f7ylr1fbJt3XPY6/rjxkLtGffASUcO67V6zfrw/8bqiuadtKOXUG370Mqps/zrbDPmye36tauFvS8wKBmxrysXSJ4b8DDDCWLFUn13NPH4+ITNH/xylTP/d9LH+toXLzatrglaPnfrCwj7pV/N23Ttp17fP1cWSfVc08f37pjtzZs3p58rgGVI1Lrp1SJoqrqDwNnzI/8vj5+4oQkKXu20BUughk25szv0+qV0//7G2kzYtxkjf99pooUKqCPXk250t7N8eMnNMb/0MZN116mXLl8q/wTExO1edtObdi8XQkJofObvfsPSpJKFk99MWbJ4md+X/01Z1FYc0TWY62dJWmVo9lZvnuytXZzBk0p2oZKcj7h+Iq/lLUrY8ylkkZKMgHN+ySFVQ48Pay105VydXY7Y8ybwa7xr+p1hqcnlLL8e2rekxT4H7c5JE3wr6IPNm6spIGSbnQcmmOtnZiGsQO1d2mLWmBrjKnhf1Aif5jnN5I0XVLgXisnJT1srT2Rzum0d7zeK2lsOvsEAPiFrkkEAAAAIMuIi0/Q1u27Ne73Gfqgz9CkcrhPd7w7Xf0uXblOw8dOVumSRdW10726rHZ1WWs1aepcvffld0o4dlydXvhAja65VMWLJv9A/fQqxkbX1NWt11+hi6pXVvGihXT4SJzWb9qmfkPHadaCZfrtr3nq/PInGvSZc2FJcq9++LVmzl+qO2+5Vg+0bKzzypXSzt379OWgHzVh8kyt3bBFXd7sqe+/DPo5IYJYsWajJKlKxbKplh6u7l+lKkkr1m5I97gdnnlXq9Zv0p59B1UgX15VqVhWN157mR5r21xlSxdPd/+pyZf3zGfFBw8fSfXcg4ePJn2/fM0GNQgSdA4b87sm/jFLhQvm18evhbUVZZaTEffK8jVnzg/1gE3g8RVrNur8CmUi7mf1+s3avG2XjsbFK2+e3Kme77Rw2Wod8t8nNS5IW6i8/8AhbdiyQ9+OmqReA32VXmNicuqxB+5MUz9InwMHD+up13tKkt5/6bGklfChLF6+NmmV+0XVK+vQ4aN67aOvNWjkzzpw0Pe7JSYmpxpccbFefuKBoA+t5MubWwcOHtHBQ0ddj5928NCZ31fLV28Ia47IsgbIF04GMzCD5hF11tpTxphOkv7Smc+ps0vqZ4xpKqm3pH8kJUiqLOl+SR3lW0Ee6Elr7cGMmbUel2+v7MC9wF8zxjSU9Jn/2CFJ50m6S1JnSc6nTd6w1q4Pd0Br7UFjzGOSAvd1Li5ptjFmgKRhklZIipNUWr5A+mn5fmaB9ktqHe64gfzl1p17lCy11i6IpL8gikr6QtIHxpjfJf0qaZGkfyUdlJRfUkn5ytrfJekGlz4esdbOSc8k/A9A1HY0D7XWUsoEAKKEcBoAAADI4gaOmKgOz7wb9PiLnduodfOb0jXG30tXq27tavpjeE8VyJ83qf3KurVUpWI5tXnyLR06fFTfjf5VXR6+J9m12bNn16qpQ133BL7uqkvU4Z7b9PpH/fXWZwP07ahJeuXJdqmWMp05f6neef5hvfxku2Ttja+/Uo3bdNWvU+dq5IQp6vnW/hRBOYJLSDimPfsOSJLKlS6R6rmFCxVQ3jy5dTQuXpu37Ur32FNmLUz6fu/+g9q7/6DmLFyuj78aps/efFKPtPEunAsMEKfOXqSuj9znet7x4yc0Z+HypNebtu50PW//gUN6+g1fsPVet0fPyXswo+6VLdt3J31frlTqDymUD5jH5u3J/2yS9RPiYYfT/VhrtWX77qRy4eF694vBSd/ffXujkOe379Jdg35w3yI1T+5c+vbzV1XpvLJpmgPS5/nuvbVj115dU+8idbzv9rCvC3wI4tSpU7qsSUet+XdLsnOOHz+h36fN1+TpC9TjxUf0QsDe4qfVqFJRsxYs04q1G7V7b/B/x/6ac2Yv8k3b3H8f4ZzxraTu8oW2Tgcl/Zix04kua+0s/0rpAUq+GvoO/1co3a21QzyZnAtr7Xp/yfNxkmIDDjXwf4XyraQeEYz7gzHmBUnvBzTnkPSw/yuUg5JapCUUd2gqX3gcyKsy17n94zVNwzUnJHWy1g6MwvgdXNoo6Q0AUURZbwAAAOAcVefCCzR3fD/16PaojDGhLwjhm4+6JQumT2vd/CaVKVlMkjQt4MPy04wxrsF0oNe6tFexIoVkrQ25d2bd2tX00hMPuI7zjD8YP3kyUbMW/JNqP0jucEBJ9cDVxMHkzePbt/fI0fiIx6x0Xhk9+8h9GtW3u+aO76e54/tpWO831er262WMUcKx43r0xY/U97sxEY8RStVKFZICyAmTZ2n63JT3sCR93HdYUiArSYePuJegf+6d3tq5e5+uqltLD98fzmfqWU9G3SuBP+NQ45wew22cZP2EWAmdvJ/Q2wwEGjVhikZOmCLJ93vqribXpen6QPc2u0Erpw5JVx9Iu2lzFuvr78crR47s+r8ez6Xp3859B85sEfB+7yFa8+8WNW54heaO76eEdX9o1+Jx6tPjWRUskE/WWr3Y4/+SyoAHuuOmayT5yoG/8kE/17HWrN+sASMmJL0O9vsI5wZr7TZJk4IcHmatjfwf4rOEtXaQpBaSDoQ6N8AxSU9Za1MvueMBa+1v8q3a3ZaGy07JtwK+vT1dVijt434g6QFJh9N46T+SrrTW/hnJuH7tHa9PSPouHf1F01JJV0cjmPavEHc+qbjYWrvQ7XwAQGQIpwEAAIAs7s5brtXS3wdr6e+DNXd8P33/5Rtq3riBFv2zRvd1fkPjf5+R7jEuql5ZtWtWcT1mjNEltapKktZvCv0Z3alTp7Rtxx6tWrdJy1au17KV67VizcakFY2Ll69N9frWd94UNDAI3Ps2nLngjNPlaCUpJmfoIluxMTklSfFh7KHqpnnjBlo7fbg+fLWz7mpynerVqaF6dWronjtu0Ij/e1tjB7ynnP55dHnzC0/3EX/nOd+Co1OnTum2ds/ry4GjtGPXXp04cVJr1m9Wlzd66uX3+yrG/54l9/f91+xF+mb4BH+w9WxUHgo5G2XUvZJsnICfvesYsTFJ3zvHSVM/McH7Sc2KNRvUoauvgkXuXLH69vNXw/rz7/58p6Tf3zN+6qM+PZ7VpRdV1bAxk9X68Te1Zn1W3UY26zl+/IQ6vfCBrLXq8tA9qlW9UpquPxp3Jh9MOHZcNzWop/GDPlC9OjUUGxuj4kUL69G2d2r8wPeVLZvv47hu730lZ0b12APNVdZfKaDvkLFq++TbWrJ8rY4fP6G9+w/q25G/qEHLx3U0LiHpd2R8ApVm/wOCrdocmJGT8JK19kdJ1eQrjZ1aSB0vabCki6y1PTNibm6stTMkVZf0pqQdqZx6Qr7V7fWstd2stafSOe63kqrK93Pan9qpkhbIF2bXttaujHRMY0wpSY0dzROttbvdzk+HfyR1k6/Me0KIcxMlTZWv1Hgda+38KM3hDqVcIT4wSn0DAPwo6w0AAABkcYUK5lehgvmTXterU0P3NrtR3478Re26dFezB7up/0cvqv3dTSIeo3qV1EvbFinkG/9wkJWG1loNGf2r+g8brzkLl6ca+uzZl/qWgdUrB9/HtUihM9v/sZLsjK3bd2v/QfdFNoUL5lfZ0sWVKyDcO37iZMg+jx0/IckXxEWiYIF8qR6//cZr9NrT7fXqh18rLj5B/YeNT1HKPVpa3n693uzaUa9/3F+HDh/V4698qsdf+TTZOUUKFdCLndvo+e69JUn58yXf7vKYf991a62e6tgq6MMc54KMuleSjePvI+gYAQG0cxxnP7lSmcex48H7CWbbjj1q8sBzOnwkTsYYffNxN9W4oGJY15YtXTzZvupXX3aRHm7dVJ1f/kRffTdGS00iXQAAIABJREFUVzTtpD9/6KmLa14QVn+I3LtfDNbKtRtVoWxJvf6MW0XX1AXeZ5Jvv+rs2VNWYa5/+cW669YGGjlhilas2aClK9Yl+31RsEA+jfmmh5o88Jx27dm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\n",
      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# iv值较低，假设我们要 'F' 淡出分出一组来提高iv\n",
    "\n",
    "#设置分组\n",
    "rule = {'var_d5':[['O'],['F'], ['M'], ['nan']]}\n",
    "\n",
    "#调整分箱\n",
    "fb.manual_bin(rule)\n",
    "\n",
    "#查看手动分箱后稳定性\n",
    "plot_var_bin_summary(fb.transform(df_selected[[col,'target']], labels=True), cols='var_d5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<autobmt.transformer.FeatureBin at 0x1429a8198>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#设置分组\n",
    "rule = {'var_d5':[['O','F'], ['M'], ['nan']]}\n",
    "#调整分箱\n",
    "fb.manual_bin(rule)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3.3 查看特征分箱后详情，如：好坏比、ks、woe、iv等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>range</th>\n",
       "      <th>var_name</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>total</th>\n",
       "      <th>positive_rate</th>\n",
       "      <th>negative_rate</th>\n",
       "      <th>odds</th>\n",
       "      <th>positive_pct</th>\n",
       "      <th>negative_pct</th>\n",
       "      <th>total_pct</th>\n",
       "      <th>cum_negative_pct</th>\n",
       "      <th>cum_total_pct</th>\n",
       "      <th>cum_positive_pct</th>\n",
       "      <th>cum_positive_rate</th>\n",
       "      <th>ks</th>\n",
       "      <th>lift</th>\n",
       "      <th>cum_lift</th>\n",
       "      <th>woe</th>\n",
       "      <th>iv</th>\n",
       "      <th>IV</th>\n",
       "      <th>range_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.[-inf ~ 1.0)</td>\n",
       "      <td>var_d1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>505</td>\n",
       "      <td>26698</td>\n",
       "      <td>27203</td>\n",
       "      <td>0.018564</td>\n",
       "      <td>0.981436</td>\n",
       "      <td>0.018915</td>\n",
       "      <td>0.543595</td>\n",
       "      <td>0.626023</td>\n",
       "      <td>0.624266</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021319</td>\n",
       "      <td>0.082428</td>\n",
       "      <td>0.870776</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.141182</td>\n",
       "      <td>0.011637</td>\n",
       "      <td>0.028056</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.[1.0 ~ inf)</td>\n",
       "      <td>var_d1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>424</td>\n",
       "      <td>15949</td>\n",
       "      <td>16373</td>\n",
       "      <td>0.025896</td>\n",
       "      <td>0.974104</td>\n",
       "      <td>0.026585</td>\n",
       "      <td>0.456405</td>\n",
       "      <td>0.373977</td>\n",
       "      <td>0.375734</td>\n",
       "      <td>0.373977</td>\n",
       "      <td>0.375734</td>\n",
       "      <td>0.456405</td>\n",
       "      <td>0.025896</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.214701</td>\n",
       "      <td>1.214701</td>\n",
       "      <td>0.199186</td>\n",
       "      <td>0.016418</td>\n",
       "      <td>0.028056</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.[-inf ~ 1.0)</td>\n",
       "      <td>var_d2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>433</td>\n",
       "      <td>14756</td>\n",
       "      <td>15189</td>\n",
       "      <td>0.028507</td>\n",
       "      <td>0.971493</td>\n",
       "      <td>0.029344</td>\n",
       "      <td>0.466093</td>\n",
       "      <td>0.346003</td>\n",
       "      <td>0.348563</td>\n",
       "      <td>0.346003</td>\n",
       "      <td>0.348563</td>\n",
       "      <td>0.466093</td>\n",
       "      <td>0.028507</td>\n",
       "      <td>0.120089</td>\n",
       "      <td>1.337182</td>\n",
       "      <td>1.337182</td>\n",
       "      <td>0.297936</td>\n",
       "      <td>0.035779</td>\n",
       "      <td>0.320862</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.[1.0 ~ 2.0)</td>\n",
       "      <td>var_d2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>226</td>\n",
       "      <td>6348</td>\n",
       "      <td>6574</td>\n",
       "      <td>0.034378</td>\n",
       "      <td>0.965622</td>\n",
       "      <td>0.035602</td>\n",
       "      <td>0.243272</td>\n",
       "      <td>0.148850</td>\n",
       "      <td>0.150863</td>\n",
       "      <td>0.494853</td>\n",
       "      <td>0.499426</td>\n",
       "      <td>0.709365</td>\n",
       "      <td>0.030281</td>\n",
       "      <td>0.214512</td>\n",
       "      <td>1.612540</td>\n",
       "      <td>1.420360</td>\n",
       "      <td>0.491243</td>\n",
       "      <td>0.046384</td>\n",
       "      <td>0.320862</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.[2.0 ~ 3.0)</td>\n",
       "      <td>var_d2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>216</td>\n",
       "      <td>11851</td>\n",
       "      <td>12067</td>\n",
       "      <td>0.017900</td>\n",
       "      <td>0.982100</td>\n",
       "      <td>0.018226</td>\n",
       "      <td>0.232508</td>\n",
       "      <td>0.277886</td>\n",
       "      <td>0.276918</td>\n",
       "      <td>0.772739</td>\n",
       "      <td>0.776345</td>\n",
       "      <td>0.941873</td>\n",
       "      <td>0.025865</td>\n",
       "      <td>0.169134</td>\n",
       "      <td>0.839626</td>\n",
       "      <td>1.213215</td>\n",
       "      <td>-0.178286</td>\n",
       "      <td>0.008090</td>\n",
       "      <td>0.320862</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.[1.0 ~ 2.0)</td>\n",
       "      <td>var_l_119</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>7199</td>\n",
       "      <td>7269</td>\n",
       "      <td>0.009630</td>\n",
       "      <td>0.990370</td>\n",
       "      <td>0.009724</td>\n",
       "      <td>0.075350</td>\n",
       "      <td>0.168804</td>\n",
       "      <td>0.166812</td>\n",
       "      <td>0.281473</td>\n",
       "      <td>0.281049</td>\n",
       "      <td>0.261572</td>\n",
       "      <td>0.019842</td>\n",
       "      <td>0.073553</td>\n",
       "      <td>0.451705</td>\n",
       "      <td>0.930697</td>\n",
       "      <td>-0.806599</td>\n",
       "      <td>0.075380</td>\n",
       "      <td>0.112883</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.[2.0 ~ inf)</td>\n",
       "      <td>var_l_119</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>173</td>\n",
       "      <td>4805</td>\n",
       "      <td>4978</td>\n",
       "      <td>0.034753</td>\n",
       "      <td>0.965247</td>\n",
       "      <td>0.036004</td>\n",
       "      <td>0.186222</td>\n",
       "      <td>0.112669</td>\n",
       "      <td>0.114237</td>\n",
       "      <td>0.112669</td>\n",
       "      <td>0.114237</td>\n",
       "      <td>0.186222</td>\n",
       "      <td>0.034753</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.630132</td>\n",
       "      <td>1.630132</td>\n",
       "      <td>0.502483</td>\n",
       "      <td>0.036959</td>\n",
       "      <td>0.112883</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.[-inf ~ 1.0)</td>\n",
       "      <td>var_l_123</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>673</td>\n",
       "      <td>28310</td>\n",
       "      <td>28983</td>\n",
       "      <td>0.023221</td>\n",
       "      <td>0.976779</td>\n",
       "      <td>0.023773</td>\n",
       "      <td>0.724435</td>\n",
       "      <td>0.663822</td>\n",
       "      <td>0.665114</td>\n",
       "      <td>0.663822</td>\n",
       "      <td>0.665114</td>\n",
       "      <td>0.724435</td>\n",
       "      <td>0.023221</td>\n",
       "      <td>0.060613</td>\n",
       "      <td>1.089189</td>\n",
       "      <td>1.089189</td>\n",
       "      <td>0.087378</td>\n",
       "      <td>0.005296</td>\n",
       "      <td>0.094210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.[1.0 ~ 2.0)</td>\n",
       "      <td>var_l_123</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>8590</td>\n",
       "      <td>8680</td>\n",
       "      <td>0.010369</td>\n",
       "      <td>0.989631</td>\n",
       "      <td>0.010477</td>\n",
       "      <td>0.096878</td>\n",
       "      <td>0.201421</td>\n",
       "      <td>0.199192</td>\n",
       "      <td>0.865243</td>\n",
       "      <td>0.864306</td>\n",
       "      <td>0.821313</td>\n",
       "      <td>0.020259</td>\n",
       "      <td>-0.043929</td>\n",
       "      <td>0.486356</td>\n",
       "      <td>0.950257</td>\n",
       "      <td>-0.731941</td>\n",
       "      <td>0.076519</td>\n",
       "      <td>0.094210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.[2.0 ~ inf)</td>\n",
       "      <td>var_l_123</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>166</td>\n",
       "      <td>5747</td>\n",
       "      <td>5913</td>\n",
       "      <td>0.028074</td>\n",
       "      <td>0.971926</td>\n",
       "      <td>0.028885</td>\n",
       "      <td>0.178687</td>\n",
       "      <td>0.134757</td>\n",
       "      <td>0.135694</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021319</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.316837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.282158</td>\n",
       "      <td>0.012395</td>\n",
       "      <td>0.094210</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             range   var_name  min  max  positive  negative  total  \\\n",
       "0   0.[-inf ~ 1.0)     var_d1    0    0       505     26698  27203   \n",
       "1    1.[1.0 ~ inf)     var_d1    1    1       424     15949  16373   \n",
       "0   0.[-inf ~ 1.0)     var_d2    0    0       433     14756  15189   \n",
       "1    1.[1.0 ~ 2.0)     var_d2    1    1       226      6348   6574   \n",
       "2    2.[2.0 ~ 3.0)     var_d2    2    2       216     11851  12067   \n",
       "..             ...        ...  ...  ...       ...       ...    ...   \n",
       "1    1.[1.0 ~ 2.0)  var_l_119    1    1        70      7199   7269   \n",
       "2    2.[2.0 ~ inf)  var_l_119    2    2       173      4805   4978   \n",
       "0   0.[-inf ~ 1.0)  var_l_123    0    0       673     28310  28983   \n",
       "1    1.[1.0 ~ 2.0)  var_l_123    1    1        90      8590   8680   \n",
       "2    2.[2.0 ~ inf)  var_l_123    2    2       166      5747   5913   \n",
       "\n",
       "    positive_rate  negative_rate      odds  positive_pct  negative_pct  \\\n",
       "0        0.018564       0.981436  0.018915      0.543595      0.626023   \n",
       "1        0.025896       0.974104  0.026585      0.456405      0.373977   \n",
       "0        0.028507       0.971493  0.029344      0.466093      0.346003   \n",
       "1        0.034378       0.965622  0.035602      0.243272      0.148850   \n",
       "2        0.017900       0.982100  0.018226      0.232508      0.277886   \n",
       "..            ...            ...       ...           ...           ...   \n",
       "1        0.009630       0.990370  0.009724      0.075350      0.168804   \n",
       "2        0.034753       0.965247  0.036004      0.186222      0.112669   \n",
       "0        0.023221       0.976779  0.023773      0.724435      0.663822   \n",
       "1        0.010369       0.989631  0.010477      0.096878      0.201421   \n",
       "2        0.028074       0.971926  0.028885      0.178687      0.134757   \n",
       "\n",
       "    total_pct  cum_negative_pct  cum_total_pct  cum_positive_pct  \\\n",
       "0    0.624266          1.000000       1.000000          1.000000   \n",
       "1    0.375734          0.373977       0.375734          0.456405   \n",
       "0    0.348563          0.346003       0.348563          0.466093   \n",
       "1    0.150863          0.494853       0.499426          0.709365   \n",
       "2    0.276918          0.772739       0.776345          0.941873   \n",
       "..        ...               ...            ...               ...   \n",
       "1    0.166812          0.281473       0.281049          0.261572   \n",
       "2    0.114237          0.112669       0.114237          0.186222   \n",
       "0    0.665114          0.663822       0.665114          0.724435   \n",
       "1    0.199192          0.865243       0.864306          0.821313   \n",
       "2    0.135694          1.000000       1.000000          1.000000   \n",
       "\n",
       "    cum_positive_rate        ks      lift  cum_lift       woe        iv  \\\n",
       "0            0.021319  0.082428  0.870776  1.000000 -0.141182  0.011637   \n",
       "1            0.025896 -0.000000  1.214701  1.214701  0.199186  0.016418   \n",
       "0            0.028507  0.120089  1.337182  1.337182  0.297936  0.035779   \n",
       "1            0.030281  0.214512  1.612540  1.420360  0.491243  0.046384   \n",
       "2            0.025865  0.169134  0.839626  1.213215 -0.178286  0.008090   \n",
       "..                ...       ...       ...       ...       ...       ...   \n",
       "1            0.019842  0.073553  0.451705  0.930697 -0.806599  0.075380   \n",
       "2            0.034753 -0.000000  1.630132  1.630132  0.502483  0.036959   \n",
       "0            0.023221  0.060613  1.089189  1.089189  0.087378  0.005296   \n",
       "1            0.020259 -0.043929  0.486356  0.950257 -0.731941  0.076519   \n",
       "2            0.021319  0.000000  1.316837  1.000000  0.282158  0.012395   \n",
       "\n",
       "          IV  range_num  \n",
       "0   0.028056          0  \n",
       "1   0.028056          1  \n",
       "0   0.320862          0  \n",
       "1   0.320862          1  \n",
       "2   0.320862          2  \n",
       "..       ...        ...  \n",
       "1   0.112883          1  \n",
       "2   0.112883          2  \n",
       "0   0.094210          0  \n",
       "1   0.094210          1  \n",
       "2   0.094210          2  \n",
       "\n",
       "[83 rows x 24 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from autobmt import calc_var_summary\n",
    "train_var_summary = calc_var_summary(fb.transform(df_selected)[selected_features + ['target']], fb.export(),\n",
    "                                             target='target')\n",
    "train_var_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. WOE转化"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "WOE转化在分箱调整好之后进行，步骤如下：\n",
    "\n",
    "1、*用调整好的fb转化数据:* fb.transform(dataframe, labels=False)\n",
    "\n",
    "    ps：只会转化被分箱的变量\n",
    "\n",
    "2、*初始化woe transer:* transer = autobmt.WOETransformer()\n",
    "\n",
    "3、*fit_transform:* transer.fit_transform(dataframe, target, exclude = None)\n",
    "\n",
    "    训练并输出woe转化后的数据，用于转化train数据集的数据\n",
    "\n",
    "    target: 目标列数据（非列名）\n",
    "    exclude: 不需要被WOE转化的列 注意：会转化所有列，包括未被分箱transform的列，通过 'exclude' 删去不要WOE转化的列，特别是target列\n",
    "4、*根据训练好的transer，转化test/OOT数据：*transer.transform(dataframe)\n",
    "\n",
    "根据训练好的transer输出woe转化的数据，用于转化test/OOT数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  APP_ID_C  target    var_d1    var_d2    var_d3    var_d4    var_d5  \\\n",
      "0    app_1       0 -0.141182 -0.178286  0.040255 -0.315451  0.090613   \n",
      "1    app_2       0  0.199186 -1.410248  0.040255 -0.315451 -0.456741   \n",
      "\n",
      "     var_d6    var_d7    var_d8   var_d11   var_b11   var_b15  var_b17  \\\n",
      "0  0.047145  0.365305 -0.140220 -0.152228 -0.237656 -0.639884      0.0   \n",
      "1  0.047145 -0.726954  0.193594 -0.152228  0.199186  0.209948      0.0   \n",
      "\n",
      "    var_b18   var_b19   var_b20   var_b21   var_b22   var_b24   var_l_9  \\\n",
      "0 -0.117058 -0.358365 -0.734605 -0.121860 -0.243476 -0.022264  0.033662   \n",
      "1 -0.117058 -0.358365  0.228970 -0.318066  0.123885 -0.022264  0.033662   \n",
      "\n",
      "   var_l_19  var_l_20  var_l_40  var_l_50  var_l_52  var_l_58  var_l_60  \\\n",
      "0  0.100105 -0.013857  0.027322  0.064735  0.114195  0.111096   0.13217   \n",
      "1  0.100105 -0.013857  0.027322  0.064735  0.114195  0.111096   0.13217   \n",
      "\n",
      "   var_l_64  var_l_66  var_l_68  var_l_89  var_l_91  var_l_119  var_l_123  \\\n",
      "0  0.080656   0.06406  0.091919  0.091901  0.086402   0.027322   0.087378   \n",
      "1  0.080656   0.06406  0.091919  0.091901  0.086402   0.027322   0.087378   \n",
      "\n",
      "    type  apply_time    month  \n",
      "0  train  2019-03-01  2019-03  \n",
      "1  train  2019-03-01  2019-03  \n",
      "CPU times: user 1.05 s, sys: 570 ms, total: 1.62 s\n",
      "Wall time: 1.69 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 初始化\n",
    "transer = autobmt.WoeTransformer()\n",
    "\n",
    "# fb.transform() & transer.fit_transform() 转化训练数据，并去掉target列\n",
    "train_woe = transer.fit_transform(fb.transform(df_selected), df_selected['target'], exclude=exclude_cols+['target'])\n",
    "OOT_woe = transer.transform(fb.transform(OOT))\n",
    "\n",
    "print(train_woe.head(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. stepwise"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "逐步回归特征筛选，支持向前，向后和双向（推荐）。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "autobmt.stepwise(dataframe, target='target', estimator='ols', direction='both', criterion='aic', max_iter=None, return_drop=False, exclude=None):\n",
    "    estimator (str): 用于拟合的模型，支持'ols', 'lr', 'lasso', 'ridge'\n",
    "    direction (str): 逐步回归的方向，支持'forward', 'backward', 'both' （推荐）\n",
    "    criterion (str): 评判标准，支持'aic', 'bic', 'ks', 'auc'\n",
    "    return_drop (bool): 是否返回被剔除的特征名\n",
    "    exclude (array-like): 不参与特征筛选的特征列表，非x列名\n",
    "    \n",
    "ps: 经验证，direction = 'both'效果最好。estimator = 'ols'以及criterion = 'aic'运行速度快且结果对逻辑回归建模有较好的代表性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43576, 16)\n"
     ]
    }
   ],
   "source": [
    "# 将woe转化后的数据做逐步回归\n",
    "final_data = autobmt.stepwise(train_woe,target = 'target', estimator='ols', direction = 'both', criterion = 'aic', exclude = exclude_cols)\n",
    "\n",
    "# 将选出的变量应用于test/OOT数据\n",
    "final_OOT = OOT_woe[final_data.columns]\n",
    "\n",
    "print(final_data.shape) # 逐步回归从33个变量中选出了11个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 确定建模要用的变量\n",
    "col = list(final_data.drop(exclude_cols+['target'],axis=1).columns)\n",
    "len(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看特征的psi，输出每列特征的PSI，可以用于检验WOE转化后的特征稳定性\n",
    "autobmt.psi(df_no_base, df_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "var_d2      0.000263\n",
       "var_d5      0.000005\n",
       "var_d7      0.000034\n",
       "var_d11     0.000191\n",
       "var_b11     0.000103\n",
       "var_b18     0.000026\n",
       "var_b19     0.000049\n",
       "var_b22     0.000037\n",
       "var_b24     0.000010\n",
       "var_l_20    0.000115\n",
       "var_l_68    0.000213\n",
       "Name: psi, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autobmt.psi(final_OOT[col], final_data[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 用逻辑回归建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最终入模变量个数： 11 \n",
      "入模变量： ['var_d2', 'var_d5', 'var_d7', 'var_d11', 'var_b11', 'var_b18', 'var_b19', 'var_b22', 'var_b24', 'var_l_20', 'var_l_68']\n"
     ]
    }
   ],
   "source": [
    "# 用逻辑回归建模\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "while True:  # 循环的目的是保证入模变量的系数都为整\n",
    "    lr = LogisticRegression()\n",
    "    lr.fit(final_data[col], final_data['target'])\n",
    "    drop_var = np.array(col)[np.where(lr.coef_ < 0)[1]]\n",
    "    if len(drop_var) == 0:\n",
    "        break\n",
    "    col = list(set(col) - set(drop_var))\n",
    "print('最终入模变量个数：',len(col),'\\n入模变量：',col)\n",
    "\n",
    "# 预测训练和隔月的OOT\n",
    "final_data['p'] = lr.predict_proba(final_data[col])[:,1]\n",
    "\n",
    "pred_OOT_may =lr.predict_proba(final_OOT.loc[final_OOT.month == '2019-05',col])[:,1]\n",
    "pred_OOT_june =lr.predict_proba(final_OOT.loc[final_OOT.month == '2019-06',col])[:,1]\n",
    "pred_OOT_july =lr.predict_proba(final_OOT.loc[final_OOT.month == '2019-07',col])[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train KS 0.3756802628068211\n",
      "train AUC 0.7511403790644923\n",
      "OOT结果\n",
      "5月 KS 0.38403399042558517\n",
      "6月 KS 0.3564957061359582\n",
      "7月 KS 0.38620741902325834\n"
     ]
    }
   ],
   "source": [
    "from autobmt import get_ks, get_auc\n",
    "\n",
    "print('train KS',get_ks(final_data['target'], final_data['p']))\n",
    "print('train AUC',get_auc(final_data['target'], final_data['p']))\n",
    "print('OOT结果')\n",
    "print('5月 KS',get_ks(final_OOT.loc[final_OOT.month == '2019-05','target'], pred_OOT_may))\n",
    "print('6月 KS',get_ks(final_OOT.loc[final_OOT.month == '2019-06','target'], pred_OOT_june))\n",
    "print('7月 KS',get_ks(final_OOT.loc[final_OOT.month == '2019-07','target'], pred_OOT_july))\n",
    "\n",
    "# train KS 0.3756802628068211\n",
    "# train AUC 0.7511403790644923\n",
    "# OOT结果\n",
    "# 5月 KS 0.38403399042558517\n",
    "# 6月 KS 0.3564957061359582\n",
    "# 7月 KS 0.38620741902325834"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.2 查看模型概率的psi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.14008457550241749\n",
      "0.14560835875192432\n",
      "0.14560835875192432\n"
     ]
    }
   ],
   "source": [
    "print(autobmt.psi(final_data['p'],pred_OOT_may))\n",
    "print(autobmt.psi(final_data['p'],pred_OOT_june))\n",
    "print(autobmt.psi(final_data['p'],pred_OOT_june))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 模型预测的概率分箱后信息，可应用于策略，有：概率区间，样本量，坏账率，ks，lift，iv等"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "autobmt.calc_bin_summary(df, bin_col='p',target='target',is_sort=False)\n",
    "    df (DataFrame): 含目标变量的数据集\n",
    "    bin_col (str): 需要计算的列名\n",
    "    bin (int): 分几箱\n",
    "    target (str):目标变量列名\n",
    "    method (str):分箱方法；'dt'、'chi'、'equal_freq'、'kmeans'四种供选择，默认'equal_freq'\n",
    "    is_sort (str):是否需要排序，默认True，倒序排序\n",
    "ps：positive_rate为每组正样本占比：（1）组之间的占比差距越大越好（2）可以用于观察是否有跳点（3）可以用与找最佳切点（4）可以对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>range</th>\n",
       "      <th>var_name</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>total</th>\n",
       "      <th>positive_rate</th>\n",
       "      <th>negative_rate</th>\n",
       "      <th>odds</th>\n",
       "      <th>positive_pct</th>\n",
       "      <th>negative_pct</th>\n",
       "      <th>total_pct</th>\n",
       "      <th>cum_negative_pct</th>\n",
       "      <th>cum_total_pct</th>\n",
       "      <th>cum_positive_pct</th>\n",
       "      <th>cum_positive_rate</th>\n",
       "      <th>ks</th>\n",
       "      <th>lift</th>\n",
       "      <th>cum_lift</th>\n",
       "      <th>woe</th>\n",
       "      <th>iv</th>\n",
       "      <th>IV</th>\n",
       "      <th>range_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.[0.04489865711062487 ~ inf)</td>\n",
       "      <td>p</td>\n",
       "      <td>0.044899</td>\n",
       "      <td>0.383093</td>\n",
       "      <td>320</td>\n",
       "      <td>4041</td>\n",
       "      <td>4361</td>\n",
       "      <td>0.073378</td>\n",
       "      <td>0.926622</td>\n",
       "      <td>0.079188</td>\n",
       "      <td>0.344456</td>\n",
       "      <td>0.094755</td>\n",
       "      <td>0.100078</td>\n",
       "      <td>0.094755</td>\n",
       "      <td>0.100078</td>\n",
       "      <td>0.344456</td>\n",
       "      <td>0.073378</td>\n",
       "      <td>0.249702</td>\n",
       "      <td>3.441879</td>\n",
       "      <td>3.441879</td>\n",
       "      <td>1.290677</td>\n",
       "      <td>0.322284</td>\n",
       "      <td>0.914431</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.[0.0324190922308802 ~ 0.04489865711062487)</td>\n",
       "      <td>p</td>\n",
       "      <td>0.032419</td>\n",
       "      <td>0.044892</td>\n",
       "      <td>171</td>\n",
       "      <td>4194</td>\n",
       "      <td>4365</td>\n",
       "      <td>0.039175</td>\n",
       "      <td>0.960825</td>\n",
       "      <td>0.040773</td>\n",
       "      <td>0.184069</td>\n",
       "      <td>0.098342</td>\n",
       "      <td>0.100170</td>\n",
       "      <td>0.193097</td>\n",
       "      <td>0.200248</td>\n",
       "      <td>0.528525</td>\n",
       "      <td>0.056269</td>\n",
       "      <td>0.335428</td>\n",
       "      <td>1.837568</td>\n",
       "      <td>2.639356</td>\n",
       "      <td>0.626857</td>\n",
       "      <td>0.053738</td>\n",
       "      <td>0.914431</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.[0.025990427073891872 ~ 0.0324190922308802)</td>\n",
       "      <td>p</td>\n",
       "      <td>0.025990</td>\n",
       "      <td>0.032413</td>\n",
       "      <td>113</td>\n",
       "      <td>4247</td>\n",
       "      <td>4360</td>\n",
       "      <td>0.025917</td>\n",
       "      <td>0.974083</td>\n",
       "      <td>0.026607</td>\n",
       "      <td>0.121636</td>\n",
       "      <td>0.099585</td>\n",
       "      <td>0.100055</td>\n",
       "      <td>0.292682</td>\n",
       "      <td>0.300303</td>\n",
       "      <td>0.650161</td>\n",
       "      <td>0.046156</td>\n",
       "      <td>0.357480</td>\n",
       "      <td>1.215692</td>\n",
       "      <td>2.165019</td>\n",
       "      <td>0.200023</td>\n",
       "      <td>0.004411</td>\n",
       "      <td>0.914431</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.[0.019493214038111856 ~ 0.025990427073891872)</td>\n",
       "      <td>p</td>\n",
       "      <td>0.019493</td>\n",
       "      <td>0.025972</td>\n",
       "      <td>115</td>\n",
       "      <td>4652</td>\n",
       "      <td>4767</td>\n",
       "      <td>0.024124</td>\n",
       "      <td>0.975876</td>\n",
       "      <td>0.024721</td>\n",
       "      <td>0.123789</td>\n",
       "      <td>0.109082</td>\n",
       "      <td>0.109395</td>\n",
       "      <td>0.401763</td>\n",
       "      <td>0.409698</td>\n",
       "      <td>0.773950</td>\n",
       "      <td>0.040273</td>\n",
       "      <td>0.372187</td>\n",
       "      <td>1.131578</td>\n",
       "      <td>1.889076</td>\n",
       "      <td>0.126483</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.914431</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.[0.014322256189578822 ~ 0.019493214038111856)</td>\n",
       "      <td>p</td>\n",
       "      <td>0.014322</td>\n",
       "      <td>0.019493</td>\n",
       "      <td>72</td>\n",
       "      <td>4054</td>\n",
       "      <td>4126</td>\n",
       "      <td>0.017450</td>\n",
       "      <td>0.982550</td>\n",
       "      <td>0.017760</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.095059</td>\n",
       "      <td>0.094685</td>\n",
       "      <td>0.496823</td>\n",
       "      <td>0.504383</td>\n",
       "      <td>0.851453</td>\n",
       "      <td>0.035989</td>\n",
       "      <td>0.354630</td>\n",
       "      <td>0.818531</td>\n",
       "      <td>1.688108</td>\n",
       "      <td>-0.204190</td>\n",
       "      <td>0.003585</td>\n",
       "      <td>0.914431</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.[0.011063498772759067 ~ 0.014322256189578822)</td>\n",
       "      <td>p</td>\n",
       "      <td>0.011063</td>\n",
       "      <td>0.014315</td>\n",
       "      <td>50</td>\n",
       "      <td>4124</td>\n",
       "      <td>4174</td>\n",
       "      <td>0.011979</td>\n",
       "      <td>0.988021</td>\n",
       "      <td>0.012124</td>\n",
       "      <td>0.053821</td>\n",
       "      <td>0.096701</td>\n",
       "      <td>0.095787</td>\n",
       "      <td>0.593524</td>\n",
       "      <td>0.600170</td>\n",
       "      <td>0.905274</td>\n",
       "      <td>0.032157</td>\n",
       "      <td>0.311751</td>\n",
       "      <td>0.561887</td>\n",
       "      <td>1.508364</td>\n",
       "      <td>-0.585952</td>\n",
       "      <td>0.025125</td>\n",
       "      <td>0.914431</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.[0.008110192312949157 ~ 0.011063498772759067)</td>\n",
       "      <td>p</td>\n",
       "      <td>0.008110</td>\n",
       "      <td>0.011057</td>\n",
       "      <td>30</td>\n",
       "      <td>4370</td>\n",
       "      <td>4400</td>\n",
       "      <td>0.006818</td>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.006865</td>\n",
       "      <td>0.032293</td>\n",
       "      <td>0.102469</td>\n",
       "      <td>0.100973</td>\n",
       "      <td>0.695993</td>\n",
       "      <td>0.701143</td>\n",
       "      <td>0.937567</td>\n",
       "      <td>0.028508</td>\n",
       "      <td>0.241575</td>\n",
       "      <td>0.319816</td>\n",
       "      <td>1.337199</td>\n",
       "      <td>-1.154717</td>\n",
       "      <td>0.081034</td>\n",
       "      <td>0.914431</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.[0.005379878739943169 ~ 0.008110192312949157)</td>\n",
       "      <td>p</td>\n",
       "      <td>0.005380</td>\n",
       "      <td>0.008109</td>\n",
       "      <td>29</td>\n",
       "      <td>4280</td>\n",
       "      <td>4309</td>\n",
       "      <td>0.006730</td>\n",
       "      <td>0.993270</td>\n",
       "      <td>0.006776</td>\n",
       "      <td>0.031216</td>\n",
       "      <td>0.100359</td>\n",
       "      <td>0.098885</td>\n",
       "      <td>0.796351</td>\n",
       "      <td>0.800028</td>\n",
       "      <td>0.968784</td>\n",
       "      <td>0.025816</td>\n",
       "      <td>0.172432</td>\n",
       "      <td>0.315684</td>\n",
       "      <td>1.210938</td>\n",
       "      <td>-1.167809</td>\n",
       "      <td>0.080745</td>\n",
       "      <td>0.914431</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.[0.0034238134017440256 ~ 0.005379878739943169)</td>\n",
       "      <td>p</td>\n",
       "      <td>0.003427</td>\n",
       "      <td>0.005379</td>\n",
       "      <td>20</td>\n",
       "      <td>4336</td>\n",
       "      <td>4356</td>\n",
       "      <td>0.004591</td>\n",
       "      <td>0.995409</td>\n",
       "      <td>0.004613</td>\n",
       "      <td>0.021529</td>\n",
       "      <td>0.101672</td>\n",
       "      <td>0.099963</td>\n",
       "      <td>0.898023</td>\n",
       "      <td>0.899991</td>\n",
       "      <td>0.990312</td>\n",
       "      <td>0.023459</td>\n",
       "      <td>0.092289</td>\n",
       "      <td>0.215364</td>\n",
       "      <td>1.100358</td>\n",
       "      <td>-1.552372</td>\n",
       "      <td>0.124412</td>\n",
       "      <td>0.914431</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.[-inf ~ 0.0034238134017440256)</td>\n",
       "      <td>p</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.003421</td>\n",
       "      <td>9</td>\n",
       "      <td>4349</td>\n",
       "      <td>4358</td>\n",
       "      <td>0.002065</td>\n",
       "      <td>0.997935</td>\n",
       "      <td>0.002069</td>\n",
       "      <td>0.009688</td>\n",
       "      <td>0.101977</td>\n",
       "      <td>0.100009</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021319</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096869</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.353873</td>\n",
       "      <td>0.217236</td>\n",
       "      <td>0.914431</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              range var_name       min  \\\n",
       "0                     9.[0.04489865711062487 ~ inf)        p  0.044899   \n",
       "1      8.[0.0324190922308802 ~ 0.04489865711062487)        p  0.032419   \n",
       "2     7.[0.025990427073891872 ~ 0.0324190922308802)        p  0.025990   \n",
       "3   6.[0.019493214038111856 ~ 0.025990427073891872)        p  0.019493   \n",
       "4   5.[0.014322256189578822 ~ 0.019493214038111856)        p  0.014322   \n",
       "5   4.[0.011063498772759067 ~ 0.014322256189578822)        p  0.011063   \n",
       "6   3.[0.008110192312949157 ~ 0.011063498772759067)        p  0.008110   \n",
       "7   2.[0.005379878739943169 ~ 0.008110192312949157)        p  0.005380   \n",
       "8  1.[0.0034238134017440256 ~ 0.005379878739943169)        p  0.003427   \n",
       "9                  0.[-inf ~ 0.0034238134017440256)        p  0.000368   \n",
       "\n",
       "        max  positive  negative  total  positive_rate  negative_rate  \\\n",
       "0  0.383093       320      4041   4361       0.073378       0.926622   \n",
       "1  0.044892       171      4194   4365       0.039175       0.960825   \n",
       "2  0.032413       113      4247   4360       0.025917       0.974083   \n",
       "3  0.025972       115      4652   4767       0.024124       0.975876   \n",
       "4  0.019493        72      4054   4126       0.017450       0.982550   \n",
       "5  0.014315        50      4124   4174       0.011979       0.988021   \n",
       "6  0.011057        30      4370   4400       0.006818       0.993182   \n",
       "7  0.008109        29      4280   4309       0.006730       0.993270   \n",
       "8  0.005379        20      4336   4356       0.004591       0.995409   \n",
       "9  0.003421         9      4349   4358       0.002065       0.997935   \n",
       "\n",
       "       odds  positive_pct  negative_pct  total_pct  cum_negative_pct  \\\n",
       "0  0.079188      0.344456      0.094755   0.100078          0.094755   \n",
       "1  0.040773      0.184069      0.098342   0.100170          0.193097   \n",
       "2  0.026607      0.121636      0.099585   0.100055          0.292682   \n",
       "3  0.024721      0.123789      0.109082   0.109395          0.401763   \n",
       "4  0.017760      0.077503      0.095059   0.094685          0.496823   \n",
       "5  0.012124      0.053821      0.096701   0.095787          0.593524   \n",
       "6  0.006865      0.032293      0.102469   0.100973          0.695993   \n",
       "7  0.006776      0.031216      0.100359   0.098885          0.796351   \n",
       "8  0.004613      0.021529      0.101672   0.099963          0.898023   \n",
       "9  0.002069      0.009688      0.101977   0.100009          1.000000   \n",
       "\n",
       "   cum_total_pct  cum_positive_pct  cum_positive_rate        ks      lift  \\\n",
       "0       0.100078          0.344456           0.073378  0.249702  3.441879   \n",
       "1       0.200248          0.528525           0.056269  0.335428  1.837568   \n",
       "2       0.300303          0.650161           0.046156  0.357480  1.215692   \n",
       "3       0.409698          0.773950           0.040273  0.372187  1.131578   \n",
       "4       0.504383          0.851453           0.035989  0.354630  0.818531   \n",
       "5       0.600170          0.905274           0.032157  0.311751  0.561887   \n",
       "6       0.701143          0.937567           0.028508  0.241575  0.319816   \n",
       "7       0.800028          0.968784           0.025816  0.172432  0.315684   \n",
       "8       0.899991          0.990312           0.023459  0.092289  0.215364   \n",
       "9       1.000000          1.000000           0.021319  0.000000  0.096869   \n",
       "\n",
       "   cum_lift       woe        iv        IV  range_num  \n",
       "0  3.441879  1.290677  0.322284  0.914431          9  \n",
       "1  2.639356  0.626857  0.053738  0.914431          8  \n",
       "2  2.165019  0.200023  0.004411  0.914431          7  \n",
       "3  1.889076  0.126483  0.001860  0.914431          6  \n",
       "4  1.688108 -0.204190  0.003585  0.914431          5  \n",
       "5  1.508364 -0.585952  0.025125  0.914431          4  \n",
       "6  1.337199 -1.154717  0.081034  0.914431          3  \n",
       "7  1.210938 -1.167809  0.080745  0.914431          2  \n",
       "8  1.100358 -1.552372  0.124412  0.914431          1  \n",
       "9  1.000000 -2.353873  0.217236  0.914431          0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autobmt.calc_bin_summary(final_data, bin_col='p',target='target')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.4 模型概率值转标准分"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "autobmt.to_score(x, A=404.65547022, B=72.1347520444)\n",
    "    x (float): 模型预测的概率值\n",
    "    pdo=50;rate=2;odds=15;base_score=600\n",
    "    A (float): 评分卡offset；；；offset = base_score - (pdo / np.log(rate)) * np.log(odds)\n",
    "    B (float): 评分卡factor；；；factor = pdo / np.log(rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APP_ID_C</th>\n",
       "      <th>target</th>\n",
       "      <th>var_d2</th>\n",
       "      <th>var_d5</th>\n",
       "      <th>var_d7</th>\n",
       "      <th>var_d11</th>\n",
       "      <th>var_b11</th>\n",
       "      <th>var_b18</th>\n",
       "      <th>var_b19</th>\n",
       "      <th>var_b22</th>\n",
       "      <th>var_b24</th>\n",
       "      <th>var_l_20</th>\n",
       "      <th>var_l_68</th>\n",
       "      <th>type</th>\n",
       "      <th>apply_time</th>\n",
       "      <th>month</th>\n",
       "      <th>p</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>app_1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.178286</td>\n",
       "      <td>0.090613</td>\n",
       "      <td>0.365305</td>\n",
       "      <td>-0.152228</td>\n",
       "      <td>-0.237656</td>\n",
       "      <td>-0.117058</td>\n",
       "      <td>-0.358365</td>\n",
       "      <td>-0.243476</td>\n",
       "      <td>-0.022264</td>\n",
       "      <td>-0.013857</td>\n",
       "      <td>0.091919</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>0.010948</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>app_2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.410248</td>\n",
       "      <td>-0.456741</td>\n",
       "      <td>-0.726954</td>\n",
       "      <td>-0.152228</td>\n",
       "      <td>0.199186</td>\n",
       "      <td>-0.117058</td>\n",
       "      <td>-0.358365</td>\n",
       "      <td>0.123885</td>\n",
       "      <td>-0.022264</td>\n",
       "      <td>-0.013857</td>\n",
       "      <td>0.091919</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>0.002840</td>\n",
       "      <td>827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>app_3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.178286</td>\n",
       "      <td>0.090613</td>\n",
       "      <td>0.365305</td>\n",
       "      <td>-0.152228</td>\n",
       "      <td>0.453002</td>\n",
       "      <td>-0.117058</td>\n",
       "      <td>-0.358365</td>\n",
       "      <td>-0.243476</td>\n",
       "      <td>0.175490</td>\n",
       "      <td>-0.013857</td>\n",
       "      <td>-0.883896</td>\n",
       "      <td>train</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>0.010136</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  APP_ID_C  target    var_d2    var_d5    var_d7   var_d11   var_b11  \\\n",
       "0    app_1       0 -0.178286  0.090613  0.365305 -0.152228 -0.237656   \n",
       "1    app_2       0 -1.410248 -0.456741 -0.726954 -0.152228  0.199186   \n",
       "2    app_3       0 -0.178286  0.090613  0.365305 -0.152228  0.453002   \n",
       "\n",
       "    var_b18   var_b19   var_b22   var_b24  var_l_20  var_l_68   type  \\\n",
       "0 -0.117058 -0.358365 -0.243476 -0.022264 -0.013857  0.091919  train   \n",
       "1 -0.117058 -0.358365  0.123885 -0.022264 -0.013857  0.091919  train   \n",
       "2 -0.117058 -0.358365 -0.243476  0.175490 -0.013857 -0.883896  train   \n",
       "\n",
       "   apply_time    month         p  score  \n",
       "0  2019-03-01  2019-03  0.010948    730  \n",
       "1  2019-03-01  2019-03  0.002840    827  \n",
       "2  2019-03-01  2019-03  0.010136    735  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data['score'] = final_data['p'].map(autobmt.to_score)\n",
    "final_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4.1 标准分分箱后信息，可应用于策略，有：概率区间，样本量，坏账率，ks，lift，iv等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>range</th>\n",
       "      <th>var_name</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>total</th>\n",
       "      <th>positive_rate</th>\n",
       "      <th>negative_rate</th>\n",
       "      <th>odds</th>\n",
       "      <th>positive_pct</th>\n",
       "      <th>negative_pct</th>\n",
       "      <th>total_pct</th>\n",
       "      <th>cum_negative_pct</th>\n",
       "      <th>cum_total_pct</th>\n",
       "      <th>cum_positive_pct</th>\n",
       "      <th>cum_positive_rate</th>\n",
       "      <th>ks</th>\n",
       "      <th>lift</th>\n",
       "      <th>cum_lift</th>\n",
       "      <th>woe</th>\n",
       "      <th>iv</th>\n",
       "      <th>IV</th>\n",
       "      <th>range_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.[-inf ~ 625.0)</td>\n",
       "      <td>score</td>\n",
       "      <td>439</td>\n",
       "      <td>624</td>\n",
       "      <td>318</td>\n",
       "      <td>3974</td>\n",
       "      <td>4292</td>\n",
       "      <td>0.074091</td>\n",
       "      <td>0.925909</td>\n",
       "      <td>0.080020</td>\n",
       "      <td>0.342304</td>\n",
       "      <td>0.093184</td>\n",
       "      <td>0.098495</td>\n",
       "      <td>0.093184</td>\n",
       "      <td>0.098495</td>\n",
       "      <td>0.342304</td>\n",
       "      <td>0.074091</td>\n",
       "      <td>0.249120</td>\n",
       "      <td>3.475354</td>\n",
       "      <td>3.475354</td>\n",
       "      <td>1.301126</td>\n",
       "      <td>0.324137</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.[625.0 ~ 650.0)</td>\n",
       "      <td>score</td>\n",
       "      <td>625</td>\n",
       "      <td>649</td>\n",
       "      <td>171</td>\n",
       "      <td>4238</td>\n",
       "      <td>4409</td>\n",
       "      <td>0.038784</td>\n",
       "      <td>0.961216</td>\n",
       "      <td>0.040349</td>\n",
       "      <td>0.184069</td>\n",
       "      <td>0.099374</td>\n",
       "      <td>0.101180</td>\n",
       "      <td>0.192558</td>\n",
       "      <td>0.199674</td>\n",
       "      <td>0.526372</td>\n",
       "      <td>0.056200</td>\n",
       "      <td>0.333815</td>\n",
       "      <td>1.819230</td>\n",
       "      <td>2.636157</td>\n",
       "      <td>0.616420</td>\n",
       "      <td>0.052208</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.[650.0 ~ 666.0)</td>\n",
       "      <td>score</td>\n",
       "      <td>650</td>\n",
       "      <td>665</td>\n",
       "      <td>114</td>\n",
       "      <td>4188</td>\n",
       "      <td>4302</td>\n",
       "      <td>0.026499</td>\n",
       "      <td>0.973501</td>\n",
       "      <td>0.027221</td>\n",
       "      <td>0.122713</td>\n",
       "      <td>0.098202</td>\n",
       "      <td>0.098724</td>\n",
       "      <td>0.290759</td>\n",
       "      <td>0.298398</td>\n",
       "      <td>0.649085</td>\n",
       "      <td>0.046374</td>\n",
       "      <td>0.358326</td>\n",
       "      <td>1.242986</td>\n",
       "      <td>2.175231</td>\n",
       "      <td>0.222823</td>\n",
       "      <td>0.005462</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.[666.0 ~ 687.0)</td>\n",
       "      <td>score</td>\n",
       "      <td>666</td>\n",
       "      <td>686</td>\n",
       "      <td>92</td>\n",
       "      <td>4156</td>\n",
       "      <td>4248</td>\n",
       "      <td>0.021657</td>\n",
       "      <td>0.978343</td>\n",
       "      <td>0.022137</td>\n",
       "      <td>0.099031</td>\n",
       "      <td>0.097451</td>\n",
       "      <td>0.097485</td>\n",
       "      <td>0.388210</td>\n",
       "      <td>0.395883</td>\n",
       "      <td>0.748116</td>\n",
       "      <td>0.040288</td>\n",
       "      <td>0.359906</td>\n",
       "      <td>1.015863</td>\n",
       "      <td>1.889741</td>\n",
       "      <td>0.016084</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.[687.0 ~ 710.0)</td>\n",
       "      <td>score</td>\n",
       "      <td>687</td>\n",
       "      <td>709</td>\n",
       "      <td>91</td>\n",
       "      <td>4307</td>\n",
       "      <td>4398</td>\n",
       "      <td>0.020691</td>\n",
       "      <td>0.979309</td>\n",
       "      <td>0.021128</td>\n",
       "      <td>0.097955</td>\n",
       "      <td>0.100992</td>\n",
       "      <td>0.100927</td>\n",
       "      <td>0.489202</td>\n",
       "      <td>0.496810</td>\n",
       "      <td>0.846071</td>\n",
       "      <td>0.036307</td>\n",
       "      <td>0.356869</td>\n",
       "      <td>0.970550</td>\n",
       "      <td>1.703007</td>\n",
       "      <td>-0.030534</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.[710.0 ~ 729.0)</td>\n",
       "      <td>score</td>\n",
       "      <td>710</td>\n",
       "      <td>728</td>\n",
       "      <td>55</td>\n",
       "      <td>4377</td>\n",
       "      <td>4432</td>\n",
       "      <td>0.012410</td>\n",
       "      <td>0.987590</td>\n",
       "      <td>0.012566</td>\n",
       "      <td>0.059203</td>\n",
       "      <td>0.102633</td>\n",
       "      <td>0.101707</td>\n",
       "      <td>0.591835</td>\n",
       "      <td>0.598518</td>\n",
       "      <td>0.905274</td>\n",
       "      <td>0.032246</td>\n",
       "      <td>0.313439</td>\n",
       "      <td>0.582096</td>\n",
       "      <td>1.512528</td>\n",
       "      <td>-0.550182</td>\n",
       "      <td>0.023894</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.[729.0 ~ 751.0)</td>\n",
       "      <td>score</td>\n",
       "      <td>729</td>\n",
       "      <td>750</td>\n",
       "      <td>22</td>\n",
       "      <td>3989</td>\n",
       "      <td>4011</td>\n",
       "      <td>0.005485</td>\n",
       "      <td>0.994515</td>\n",
       "      <td>0.005515</td>\n",
       "      <td>0.023681</td>\n",
       "      <td>0.093535</td>\n",
       "      <td>0.092046</td>\n",
       "      <td>0.685371</td>\n",
       "      <td>0.690564</td>\n",
       "      <td>0.928956</td>\n",
       "      <td>0.028679</td>\n",
       "      <td>0.243585</td>\n",
       "      <td>0.257277</td>\n",
       "      <td>1.345214</td>\n",
       "      <td>-1.373650</td>\n",
       "      <td>0.095955</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.[751.0 ~ 781.0)</td>\n",
       "      <td>score</td>\n",
       "      <td>751</td>\n",
       "      <td>780</td>\n",
       "      <td>37</td>\n",
       "      <td>4680</td>\n",
       "      <td>4717</td>\n",
       "      <td>0.007844</td>\n",
       "      <td>0.992156</td>\n",
       "      <td>0.007906</td>\n",
       "      <td>0.039828</td>\n",
       "      <td>0.109738</td>\n",
       "      <td>0.108248</td>\n",
       "      <td>0.795109</td>\n",
       "      <td>0.798811</td>\n",
       "      <td>0.968784</td>\n",
       "      <td>0.025855</td>\n",
       "      <td>0.173675</td>\n",
       "      <td>0.367932</td>\n",
       "      <td>1.212782</td>\n",
       "      <td>-1.013532</td>\n",
       "      <td>0.070856</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.[781.0 ~ 814.0)</td>\n",
       "      <td>score</td>\n",
       "      <td>781</td>\n",
       "      <td>813</td>\n",
       "      <td>20</td>\n",
       "      <td>4360</td>\n",
       "      <td>4380</td>\n",
       "      <td>0.004566</td>\n",
       "      <td>0.995434</td>\n",
       "      <td>0.004587</td>\n",
       "      <td>0.021529</td>\n",
       "      <td>0.102235</td>\n",
       "      <td>0.100514</td>\n",
       "      <td>0.897343</td>\n",
       "      <td>0.899325</td>\n",
       "      <td>0.990312</td>\n",
       "      <td>0.023476</td>\n",
       "      <td>0.092969</td>\n",
       "      <td>0.214184</td>\n",
       "      <td>1.101172</td>\n",
       "      <td>-1.557892</td>\n",
       "      <td>0.125731</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.[814.0 ~ inf)</td>\n",
       "      <td>score</td>\n",
       "      <td>814</td>\n",
       "      <td>900</td>\n",
       "      <td>9</td>\n",
       "      <td>4378</td>\n",
       "      <td>4387</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>0.997948</td>\n",
       "      <td>0.002056</td>\n",
       "      <td>0.009688</td>\n",
       "      <td>0.102657</td>\n",
       "      <td>0.100675</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021319</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096229</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.360519</td>\n",
       "      <td>0.219455</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               range var_name  min  max  positive  negative  total  \\\n",
       "0   0.[-inf ~ 625.0)    score  439  624       318      3974   4292   \n",
       "1  1.[625.0 ~ 650.0)    score  625  649       171      4238   4409   \n",
       "2  2.[650.0 ~ 666.0)    score  650  665       114      4188   4302   \n",
       "3  3.[666.0 ~ 687.0)    score  666  686        92      4156   4248   \n",
       "4  4.[687.0 ~ 710.0)    score  687  709        91      4307   4398   \n",
       "5  5.[710.0 ~ 729.0)    score  710  728        55      4377   4432   \n",
       "6  6.[729.0 ~ 751.0)    score  729  750        22      3989   4011   \n",
       "7  7.[751.0 ~ 781.0)    score  751  780        37      4680   4717   \n",
       "8  8.[781.0 ~ 814.0)    score  781  813        20      4360   4380   \n",
       "9    9.[814.0 ~ inf)    score  814  900         9      4378   4387   \n",
       "\n",
       "   positive_rate  negative_rate      odds  positive_pct  negative_pct  \\\n",
       "0       0.074091       0.925909  0.080020      0.342304      0.093184   \n",
       "1       0.038784       0.961216  0.040349      0.184069      0.099374   \n",
       "2       0.026499       0.973501  0.027221      0.122713      0.098202   \n",
       "3       0.021657       0.978343  0.022137      0.099031      0.097451   \n",
       "4       0.020691       0.979309  0.021128      0.097955      0.100992   \n",
       "5       0.012410       0.987590  0.012566      0.059203      0.102633   \n",
       "6       0.005485       0.994515  0.005515      0.023681      0.093535   \n",
       "7       0.007844       0.992156  0.007906      0.039828      0.109738   \n",
       "8       0.004566       0.995434  0.004587      0.021529      0.102235   \n",
       "9       0.002052       0.997948  0.002056      0.009688      0.102657   \n",
       "\n",
       "   total_pct  cum_negative_pct  cum_total_pct  cum_positive_pct  \\\n",
       "0   0.098495          0.093184       0.098495          0.342304   \n",
       "1   0.101180          0.192558       0.199674          0.526372   \n",
       "2   0.098724          0.290759       0.298398          0.649085   \n",
       "3   0.097485          0.388210       0.395883          0.748116   \n",
       "4   0.100927          0.489202       0.496810          0.846071   \n",
       "5   0.101707          0.591835       0.598518          0.905274   \n",
       "6   0.092046          0.685371       0.690564          0.928956   \n",
       "7   0.108248          0.795109       0.798811          0.968784   \n",
       "8   0.100514          0.897343       0.899325          0.990312   \n",
       "9   0.100675          1.000000       1.000000          1.000000   \n",
       "\n",
       "   cum_positive_rate        ks      lift  cum_lift       woe        iv  \\\n",
       "0           0.074091  0.249120  3.475354  3.475354  1.301126  0.324137   \n",
       "1           0.056200  0.333815  1.819230  2.636157  0.616420  0.052208   \n",
       "2           0.046374  0.358326  1.242986  2.175231  0.222823  0.005462   \n",
       "3           0.040288  0.359906  1.015863  1.889741  0.016084  0.000025   \n",
       "4           0.036307  0.356869  0.970550  1.703007 -0.030534  0.000093   \n",
       "5           0.032246  0.313439  0.582096  1.512528 -0.550182  0.023894   \n",
       "6           0.028679  0.243585  0.257277  1.345214 -1.373650  0.095955   \n",
       "7           0.025855  0.173675  0.367932  1.212782 -1.013532  0.070856   \n",
       "8           0.023476  0.092969  0.214184  1.101172 -1.557892  0.125731   \n",
       "9           0.021319  0.000000  0.096229  1.000000 -2.360519  0.219455   \n",
       "\n",
       "         IV  range_num  \n",
       "0  0.917816          0  \n",
       "1  0.917816          1  \n",
       "2  0.917816          2  \n",
       "3  0.917816          3  \n",
       "4  0.917816          4  \n",
       "5  0.917816          5  \n",
       "6  0.917816          6  \n",
       "7  0.917816          7  \n",
       "8  0.917816          8  \n",
       "9  0.917816          9  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autobmt.calc_bin_summary(final_data, bin_col='score',target='target',is_sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. 统计入模变量相关信息，如：系数，p值，t值，vif，iv，psi，缺失率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型截距： -3.833522889351234\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>p_value</th>\n",
       "      <th>t_value</th>\n",
       "      <th>vif</th>\n",
       "      <th>train_iv</th>\n",
       "      <th>test_iv</th>\n",
       "      <th>train_test_psi</th>\n",
       "      <th>cn</th>\n",
       "      <th>miss_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>var_d2</th>\n",
       "      <td>0.733823</td>\n",
       "      <td>4.743542e-18</td>\n",
       "      <td>8.663152</td>\n",
       "      <td>1.180765</td>\n",
       "      <td>0.320862</td>\n",
       "      <td>0.320862</td>\n",
       "      <td>0.000263</td>\n",
       "      <td></td>\n",
       "      <td>0.054434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_d5</th>\n",
       "      <td>0.686785</td>\n",
       "      <td>1.947316e-06</td>\n",
       "      <td>4.759462</td>\n",
       "      <td>1.090034</td>\n",
       "      <td>0.038446</td>\n",
       "      <td>0.038446</td>\n",
       "      <td>0.000005</td>\n",
       "      <td></td>\n",
       "      <td>0.010763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_d7</th>\n",
       "      <td>0.704078</td>\n",
       "      <td>2.436844e-17</td>\n",
       "      <td>8.474351</td>\n",
       "      <td>1.114386</td>\n",
       "      <td>0.207174</td>\n",
       "      <td>0.207174</td>\n",
       "      <td>0.000034</td>\n",
       "      <td></td>\n",
       "      <td>0.016018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_d11</th>\n",
       "      <td>0.897302</td>\n",
       "      <td>9.324294e-24</td>\n",
       "      <td>10.054417</td>\n",
       "      <td>1.079282</td>\n",
       "      <td>0.108995</td>\n",
       "      <td>0.108995</td>\n",
       "      <td>0.000191</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_b11</th>\n",
       "      <td>0.516235</td>\n",
       "      <td>2.594739e-04</td>\n",
       "      <td>3.653024</td>\n",
       "      <td>1.232029</td>\n",
       "      <td>0.112845</td>\n",
       "      <td>0.112845</td>\n",
       "      <td>0.000103</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_b18</th>\n",
       "      <td>0.636135</td>\n",
       "      <td>3.924099e-11</td>\n",
       "      <td>6.608608</td>\n",
       "      <td>1.157381</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000026</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_b19</th>\n",
       "      <td>0.658623</td>\n",
       "      <td>2.344575e-12</td>\n",
       "      <td>7.014304</td>\n",
       "      <td>1.165746</td>\n",
       "      <td>0.147523</td>\n",
       "      <td>0.147523</td>\n",
       "      <td>0.000049</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_b22</th>\n",
       "      <td>1.268975</td>\n",
       "      <td>5.810922e-07</td>\n",
       "      <td>4.998142</td>\n",
       "      <td>1.567244</td>\n",
       "      <td>0.030087</td>\n",
       "      <td>0.030087</td>\n",
       "      <td>0.000037</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_b24</th>\n",
       "      <td>2.030654</td>\n",
       "      <td>2.915887e-06</td>\n",
       "      <td>4.677271</td>\n",
       "      <td>1.274695</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.000010</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_l_20</th>\n",
       "      <td>0.974403</td>\n",
       "      <td>6.219273e-06</td>\n",
       "      <td>4.519350</td>\n",
       "      <td>1.014357</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>0.000115</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var_l_68</th>\n",
       "      <td>0.856661</td>\n",
       "      <td>1.665030e-14</td>\n",
       "      <td>7.676800</td>\n",
       "      <td>1.083688</td>\n",
       "      <td>0.144309</td>\n",
       "      <td>0.144309</td>\n",
       "      <td>0.000213</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              coef       p_value    t_value       vif  train_iv   test_iv  \\\n",
       "var_d2    0.733823  4.743542e-18   8.663152  1.180765  0.320862  0.320862   \n",
       "var_d5    0.686785  1.947316e-06   4.759462  1.090034  0.038446  0.038446   \n",
       "var_d7    0.704078  2.436844e-17   8.474351  1.114386  0.207174  0.207174   \n",
       "var_d11   0.897302  9.324294e-24  10.054417  1.079282  0.108995  0.108995   \n",
       "var_b11   0.516235  2.594739e-04   3.653024  1.232029  0.112845  0.112845   \n",
       "var_b18   0.636135  3.924099e-11   6.608608  1.157381  0.000000  0.000000   \n",
       "var_b19   0.658623  2.344575e-12   7.014304  1.165746  0.147523  0.147523   \n",
       "var_b22   1.268975  5.810922e-07   4.998142  1.567244  0.030087  0.030087   \n",
       "var_b24   2.030654  2.915887e-06   4.677271  1.274695  0.003906  0.003906   \n",
       "var_l_20  0.974403  6.219273e-06   4.519350  1.014357  0.000878  0.000878   \n",
       "var_l_68  0.856661  1.665030e-14   7.676800  1.083688  0.144309  0.144309   \n",
       "\n",
       "          train_test_psi cn  miss_rate  \n",
       "var_d2          0.000263      0.054434  \n",
       "var_d5          0.000005      0.010763  \n",
       "var_d7          0.000034      0.016018  \n",
       "var_d11         0.000191      0.000000  \n",
       "var_b11         0.000103      0.000000  \n",
       "var_b18         0.000026      0.000000  \n",
       "var_b19         0.000049      0.000000  \n",
       "var_b22         0.000037      0.000000  \n",
       "var_b24         0.000010      0.000000  \n",
       "var_l_20        0.000115      0.000000  \n",
       "var_l_68        0.000213      0.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psi_v = autobmt.psi(train_woe[col], OOT_woe[col])\n",
    "psi_v.name = 'train_test_psi'\n",
    "train_iv = train_var_summary[['var_name', 'IV']].rename(columns={'IV': 'train_iv'}).drop_duplicates().set_index(\n",
    "    'var_name')\n",
    "test_iv = train_var_summary[['var_name', 'IV']].rename(columns={'IV': 'test_iv'}).drop_duplicates().set_index(\n",
    "    'var_name')\n",
    "var_miss = select_log_df[['feature', 'cn', 'miss_rate']].drop_duplicates().set_index('feature')\n",
    "\n",
    "coef_s = {}\n",
    "for idx, key in enumerate(col):\n",
    "    coef_s[key] = lr.coef_[0][idx]\n",
    "var_coef = pd.Series(coef_s, name='coef')\n",
    "var_vif = autobmt.get_vif(final_data[col])\n",
    "statsm = autobmt.StatsModel(estimator='ols', intercept=True)\n",
    "t_p_c_value = statsm.stats(final_data[col], final_data['target'])\n",
    "p_value = pd.Series(t_p_c_value['p_value'], name='p_value')\n",
    "t_value = pd.Series(t_p_c_value['t_value'], name='t_value')\n",
    "\n",
    "var_info = pd.concat([var_coef, p_value, t_value, var_vif, train_iv, test_iv, psi_v, var_miss],\n",
    "                         axis=1).dropna(subset=['coef'])\n",
    "print('模型截距：',lr.intercept_[0])\n",
    "var_info "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. ScoreCard"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "逻辑回归模型转标准评分卡，支持传入逻辑回归参数，进行调参。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "- combiner: 传入训练好的 autobmt.FeatureBin 对象\n",
    "\n",
    "- transer: 传入先前训练的 autobmt.WOETransformer 对象\n",
    "\n",
    "- pdo、rate、odds、base_score:\n",
    " e.g. pdo=50, rate=2, odds=15,base_score=600\n",
    "      实际意义为当比率为1/15，输出基准评分600，当比率为基准比率2倍时，基准分下降50分\n",
    "\n",
    "- card: 支持传入专家评分卡\n",
    "\n",
    "- **kwargs: 支持传入逻辑回归参数（参数详见 sklearn.linear_model.LogisticRegression）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScoreCard(card={'var_b11': {'bins': array([-9999,     1,    52]),\n",
       "                            'scores': array([54.50860959, 70.77593349, 45.05686182, 86.87080081]),\n",
       "                            'weight': 0.5162347153417548,\n",
       "                            'woes': [0.1991855165646647, -0.23765646087901432,\n",
       "                                     0.4530023222942999, -0.6698672974092893]},\n",
       "                'var_b18': {'bins': array([2]),\n",
       "                            'scores': array([67.29748845, 29.54282027]),\n",
       "                            'weight': 0.636135303840287,\n",
       "                            'woes': [-0.1170...\n",
       "                'var_l_68': {'bins': array([0.00025569, 0.00204551, 0.00741498, 0.01994375]),\n",
       "                             'scores': array([ 56.24581196,  17.51641951,  52.87896327,  80.55262987,\n",
       "       116.54636234]),\n",
       "                             'weight': 0.8566607331587649,\n",
       "                             'woes': [0.09191939927294365, 0.7186592074945422,\n",
       "                                      0.14640355126124754, -0.3014265695175748,\n",
       "                                      -0.8838964602863048]}},\n",
       "          combiner=<autobmt.transformer.FeatureBin object at 0x1429a8198>,\n",
       "          transer=<autobmt.transformer.WoeTransformer object at 0x1429d5160>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card = autobmt.ScoreCard(\n",
    "    combiner = fb,\n",
    "    transer = transer,\n",
    "    #class_weight = 'balanced',\n",
    "    #C=0.1,\n",
    "    #base_score = 600,\n",
    "    #odds = 15 ,\n",
    "    #pdo = 50,\n",
    "    #rate = 2\n",
    ")\n",
    "card.fit(final_data[col], final_data['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. 使用评分卡直接给原始数据打分"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "评分卡在 fit 时使用 WOE 转换后的数据来计算最终的分数，分数一旦计算完成，便无需 WOE 值，可以直接使用 原始数据 进行评分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([729.52339539, 827.43252271, 735.13775954, ..., 755.12887476,\n",
       "       760.59522142, 726.16206247])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 直接使用原始数据进行评分\n",
    "card.predict(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.1 导出评分卡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>value</th>\n",
       "      <th>score</th>\n",
       "      <th>woe</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>var_d2</td>\n",
       "      <td>[-inf ~ 747.0)</td>\n",
       "      <td>46.15</td>\n",
       "      <td>0.297936</td>\n",
       "      <td>0.733823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>var_d2</td>\n",
       "      <td>[747.0 ~ 782.0)</td>\n",
       "      <td>35.92</td>\n",
       "      <td>0.491243</td>\n",
       "      <td>0.733823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>var_d2</td>\n",
       "      <td>[782.0 ~ 820.0)</td>\n",
       "      <td>71.36</td>\n",
       "      <td>-0.178286</td>\n",
       "      <td>0.733823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>var_d2</td>\n",
       "      <td>[820.0 ~ inf)</td>\n",
       "      <td>136.58</td>\n",
       "      <td>-1.410248</td>\n",
       "      <td>0.733823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>var_d2</td>\n",
       "      <td>nan</td>\n",
       "      <td>127.06</td>\n",
       "      <td>-1.230491</td>\n",
       "      <td>0.733823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>var_d5</td>\n",
       "      <td>O,F</td>\n",
       "      <td>84.55</td>\n",
       "      <td>-0.456741</td>\n",
       "      <td>0.686785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>var_d5</td>\n",
       "      <td>M</td>\n",
       "      <td>57.44</td>\n",
       "      <td>0.090613</td>\n",
       "      <td>0.686785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>var_d5</td>\n",
       "      <td>nan</td>\n",
       "      <td>87.66</td>\n",
       "      <td>-0.519364</td>\n",
       "      <td>0.686785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>var_d7</td>\n",
       "      <td>LARGE FLEET OPERATOR,COMPANY,STRATEGIC TRANSPR...</td>\n",
       "      <td>98.85</td>\n",
       "      <td>-0.726954</td>\n",
       "      <td>0.704078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>var_d7</td>\n",
       "      <td>SAL(RETIRAL AGE 60),SERVICES,SAL(RETIRAL AGE 5...</td>\n",
       "      <td>64.14</td>\n",
       "      <td>-0.043591</td>\n",
       "      <td>0.704078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>var_d7</td>\n",
       "      <td>PROPRIETOR,TRADING,STRATEGIC CAPTIVE,SELF-EMPL...</td>\n",
       "      <td>43.37</td>\n",
       "      <td>0.365305</td>\n",
       "      <td>0.704078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>var_d7</td>\n",
       "      <td>nan</td>\n",
       "      <td>73.07</td>\n",
       "      <td>-0.219368</td>\n",
       "      <td>0.704078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>var_d11</td>\n",
       "      <td>N</td>\n",
       "      <td>71.78</td>\n",
       "      <td>-0.152228</td>\n",
       "      <td>0.897302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>var_d11</td>\n",
       "      <td>U</td>\n",
       "      <td>15.16</td>\n",
       "      <td>0.722515</td>\n",
       "      <td>0.897302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>var_b11</td>\n",
       "      <td>[-inf ~ -9999)</td>\n",
       "      <td>54.51</td>\n",
       "      <td>0.199186</td>\n",
       "      <td>0.516235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>var_b11</td>\n",
       "      <td>[-9999 ~ 1)</td>\n",
       "      <td>70.78</td>\n",
       "      <td>-0.237656</td>\n",
       "      <td>0.516235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>var_b11</td>\n",
       "      <td>[1 ~ 52)</td>\n",
       "      <td>45.06</td>\n",
       "      <td>0.453002</td>\n",
       "      <td>0.516235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>var_b11</td>\n",
       "      <td>[52 ~ inf)</td>\n",
       "      <td>86.87</td>\n",
       "      <td>-0.669867</td>\n",
       "      <td>0.516235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>var_b18</td>\n",
       "      <td>[-inf ~ 2)</td>\n",
       "      <td>67.30</td>\n",
       "      <td>-0.117058</td>\n",
       "      <td>0.636135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>var_b18</td>\n",
       "      <td>[2 ~ inf)</td>\n",
       "      <td>29.54</td>\n",
       "      <td>0.705708</td>\n",
       "      <td>0.636135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>var_b19</td>\n",
       "      <td>[-inf ~ -9999)</td>\n",
       "      <td>56.34</td>\n",
       "      <td>0.117645</td>\n",
       "      <td>0.658623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>var_b19</td>\n",
       "      <td>[-9999 ~ 4)</td>\n",
       "      <td>78.95</td>\n",
       "      <td>-0.358365</td>\n",
       "      <td>0.658623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>var_b19</td>\n",
       "      <td>[4 ~ inf)</td>\n",
       "      <td>32.15</td>\n",
       "      <td>0.626694</td>\n",
       "      <td>0.658623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>var_b22</td>\n",
       "      <td>[-inf ~ -8888)</td>\n",
       "      <td>50.59</td>\n",
       "      <td>0.123885</td>\n",
       "      <td>1.268975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>var_b22</td>\n",
       "      <td>[-8888 ~ inf)</td>\n",
       "      <td>84.21</td>\n",
       "      <td>-0.243476</td>\n",
       "      <td>1.268975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>var_b24</td>\n",
       "      <td>[-inf ~ 0.806656312)</td>\n",
       "      <td>65.19</td>\n",
       "      <td>-0.022264</td>\n",
       "      <td>2.030654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>var_b24</td>\n",
       "      <td>[0.806656312 ~ inf)</td>\n",
       "      <td>36.22</td>\n",
       "      <td>0.175490</td>\n",
       "      <td>2.030654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>var_l_20</td>\n",
       "      <td>[-inf ~ 0.0004042969999999)</td>\n",
       "      <td>62.90</td>\n",
       "      <td>-0.013857</td>\n",
       "      <td>0.974403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>var_l_20</td>\n",
       "      <td>[0.0004042969999999 ~ 0.003092244)</td>\n",
       "      <td>84.11</td>\n",
       "      <td>-0.315673</td>\n",
       "      <td>0.974403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>var_l_20</td>\n",
       "      <td>[0.003092244 ~ inf)</td>\n",
       "      <td>27.41</td>\n",
       "      <td>0.491124</td>\n",
       "      <td>0.974403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>var_l_68</td>\n",
       "      <td>[-inf ~ 0.000255689)</td>\n",
       "      <td>56.25</td>\n",
       "      <td>0.091919</td>\n",
       "      <td>0.856661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>var_l_68</td>\n",
       "      <td>[0.000255689 ~ 0.0020455129999999)</td>\n",
       "      <td>17.52</td>\n",
       "      <td>0.718659</td>\n",
       "      <td>0.856661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>var_l_68</td>\n",
       "      <td>[0.0020455129999999 ~ 0.007414983)</td>\n",
       "      <td>52.88</td>\n",
       "      <td>0.146404</td>\n",
       "      <td>0.856661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>var_l_68</td>\n",
       "      <td>[0.007414983 ~ 0.019943748)</td>\n",
       "      <td>80.55</td>\n",
       "      <td>-0.301427</td>\n",
       "      <td>0.856661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>var_l_68</td>\n",
       "      <td>[0.019943748 ~ inf)</td>\n",
       "      <td>116.55</td>\n",
       "      <td>-0.883896</td>\n",
       "      <td>0.856661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     feature                                              value   score  \\\n",
       "0     var_d2                                     [-inf ~ 747.0)   46.15   \n",
       "1     var_d2                                    [747.0 ~ 782.0)   35.92   \n",
       "2     var_d2                                    [782.0 ~ 820.0)   71.36   \n",
       "3     var_d2                                      [820.0 ~ inf)  136.58   \n",
       "4     var_d2                                                nan  127.06   \n",
       "5     var_d5                                                O,F   84.55   \n",
       "6     var_d5                                                  M   57.44   \n",
       "7     var_d5                                                nan   87.66   \n",
       "8     var_d7  LARGE FLEET OPERATOR,COMPANY,STRATEGIC TRANSPR...   98.85   \n",
       "9     var_d7  SAL(RETIRAL AGE 60),SERVICES,SAL(RETIRAL AGE 5...   64.14   \n",
       "10    var_d7  PROPRIETOR,TRADING,STRATEGIC CAPTIVE,SELF-EMPL...   43.37   \n",
       "11    var_d7                                                nan   73.07   \n",
       "12   var_d11                                                  N   71.78   \n",
       "13   var_d11                                                  U   15.16   \n",
       "14   var_b11                                     [-inf ~ -9999)   54.51   \n",
       "15   var_b11                                        [-9999 ~ 1)   70.78   \n",
       "16   var_b11                                           [1 ~ 52)   45.06   \n",
       "17   var_b11                                         [52 ~ inf)   86.87   \n",
       "18   var_b18                                         [-inf ~ 2)   67.30   \n",
       "19   var_b18                                          [2 ~ inf)   29.54   \n",
       "20   var_b19                                     [-inf ~ -9999)   56.34   \n",
       "21   var_b19                                        [-9999 ~ 4)   78.95   \n",
       "22   var_b19                                          [4 ~ inf)   32.15   \n",
       "23   var_b22                                     [-inf ~ -8888)   50.59   \n",
       "24   var_b22                                      [-8888 ~ inf)   84.21   \n",
       "25   var_b24                               [-inf ~ 0.806656312)   65.19   \n",
       "26   var_b24                                [0.806656312 ~ inf)   36.22   \n",
       "27  var_l_20                        [-inf ~ 0.0004042969999999)   62.90   \n",
       "28  var_l_20                 [0.0004042969999999 ~ 0.003092244)   84.11   \n",
       "29  var_l_20                                [0.003092244 ~ inf)   27.41   \n",
       "30  var_l_68                               [-inf ~ 0.000255689)   56.25   \n",
       "31  var_l_68                 [0.000255689 ~ 0.0020455129999999)   17.52   \n",
       "32  var_l_68                 [0.0020455129999999 ~ 0.007414983)   52.88   \n",
       "33  var_l_68                        [0.007414983 ~ 0.019943748)   80.55   \n",
       "34  var_l_68                                [0.019943748 ~ inf)  116.55   \n",
       "\n",
       "         woe    weight  \n",
       "0   0.297936  0.733823  \n",
       "1   0.491243  0.733823  \n",
       "2  -0.178286  0.733823  \n",
       "3  -1.410248  0.733823  \n",
       "4  -1.230491  0.733823  \n",
       "5  -0.456741  0.686785  \n",
       "6   0.090613  0.686785  \n",
       "7  -0.519364  0.686785  \n",
       "8  -0.726954  0.704078  \n",
       "9  -0.043591  0.704078  \n",
       "10  0.365305  0.704078  \n",
       "11 -0.219368  0.704078  \n",
       "12 -0.152228  0.897302  \n",
       "13  0.722515  0.897302  \n",
       "14  0.199186  0.516235  \n",
       "15 -0.237656  0.516235  \n",
       "16  0.453002  0.516235  \n",
       "17 -0.669867  0.516235  \n",
       "18 -0.117058  0.636135  \n",
       "19  0.705708  0.636135  \n",
       "20  0.117645  0.658623  \n",
       "21 -0.358365  0.658623  \n",
       "22  0.626694  0.658623  \n",
       "23  0.123885  1.268975  \n",
       "24 -0.243476  1.268975  \n",
       "25 -0.022264  2.030654  \n",
       "26  0.175490  2.030654  \n",
       "27 -0.013857  0.974403  \n",
       "28 -0.315673  0.974403  \n",
       "29  0.491124  0.974403  \n",
       "30  0.091919  0.856661  \n",
       "31  0.718659  0.856661  \n",
       "32  0.146404  0.856661  \n",
       "33 -0.301427  0.856661  \n",
       "34 -0.883896  0.856661  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card.export(to_dataframe=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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